CN115482922A

CN115482922A - Eye pressure type detection method and device, computer equipment and storage medium

Info

Publication number: CN115482922A
Application number: CN202210932258.XA
Authority: CN
Inventors: 张青; 李树宁; 张烨; 冯慧
Original assignee: Beijing Tongren Hospital
Current assignee: Beijing Tongren Hospital
Priority date: 2022-08-04
Filing date: 2022-08-04
Publication date: 2022-12-16

Abstract

The invention provides an intraocular pressure type detection method, an intraocular pressure type detection device, computer equipment and a storage medium, wherein the intraocular pressure type detection method comprises the following steps: acquiring first eye pressure data of a target object to be detected; comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, wherein the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type; and when the first intraocular pressure data falls into a preset first type reference intraocular pressure data range or a preset second type reference intraocular pressure data range, taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected. The method provided by the invention can improve the classification accuracy of intraocular pressure abnormal types.

Description

Eye pressure type detection method and device, computer equipment and storage medium

Technical Field

The embodiment of the invention relates to the field of data processing, in particular to an eye pressure type detection method and device, computer equipment and a storage medium.

Background

Intraocular Pressure (IOP) is a major parameter in the development and progression of high-tension Glaucoma (HTG); similar to Ocular Hypertension glaucoma, the major manifestations of Ocular Hypertension (OHT) also include increased Ocular pressure, but glaucomatous optic nerve damage and/or visual field loss has not occurred, i.e. Ocular Hypertension is a pre-symptom of Ocular Hypertension glaucoma.

In the prior art, the type of the eye pressure abnormity is judged mainly by the experience of workers, and the judgment result is high in subjectivity and inaccurate.

Disclosure of Invention

The application provides an intraocular pressure type detection method, an intraocular pressure type detection device, computer equipment and a storage medium, which are used for solving the technical problem that the accuracy of a judging mode of intraocular pressure abnormal types in the prior art is low.

The invention provides an intraocular pressure type detection method in a first aspect, which comprises the following steps: acquiring first eye pressure data of a target object to be detected; comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, wherein the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type; and when the first intraocular pressure data falls into a preset first type reference intraocular pressure data range or a preset second type reference intraocular pressure data range, taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected.

The intraocular pressure type detection method provided by the embodiment of the invention can be used for acquiring intraocular pressure data of a target object to be detected, and intraocular pressure fluctuation information provided by the intraocular pressure data has more objective response to the real state of the target object. The obtained intraocular pressure data of the target object is compared with the preset type reference intraocular pressure data of the system to obtain the type of intraocular pressure abnormity of the target object, so that the differentiation of diagnosis results caused by human factors is eliminated, and the method is more objective.

Optionally, after the first eye pressure data of the target object to be detected is acquired, the method further includes: performing difference processing on the first intraocular pressure data and intraocular pressure data at a preset time point; comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types; and when the processed intraocular pressure data falls into a preset third type reference intraocular pressure data range or a preset fourth type reference intraocular pressure data range, taking the type corresponding to the corresponding parameter intraocular pressure data range as the intraocular pressure type of the target object to be detected.

Optionally, the first intraocular pressure data includes intraocular pressure data for a target duration, and the method further includes: fitting intraocular pressure data of a target duration to obtain a first intraocular pressure curve; matching the first intraocular pressure curve with a preset first type reference intraocular pressure curve and a preset second type reference intraocular pressure curve respectively, and fitting the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve in advance according to corresponding types of historical intraocular pressure data respectively to obtain the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve; and taking the type corresponding to the reference intraocular pressure curve with the highest matching degree as the intraocular pressure type of the target object to be detected.

Optionally, the first type of corresponding symptom is a pre-symptom of the second type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a first type, acquiring second intraocular pressure data of the target object to be detected in the next target time duration; fitting the second intraocular pressure data to obtain a second intraocular pressure curve; respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to a first intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to a second intraocular pressure curve; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than the first preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a second type.

Optionally, the first type of corresponding symptom is a prophase symptom of the second type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a second type, acquiring third intraocular pressure data of the target object to be detected in the next target time period; fitting the third intraocular pressure data to obtain a third intraocular pressure curve; respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the first intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the third intraocular pressure curve; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a second preset value, predicting that the intraocular pressure type of the target object to be detected is a second type.

Optionally, the first intraocular pressure data includes intraocular pressure data of a target duration, and after performing the difference processing step on the first intraocular pressure data and intraocular pressure data at a preset time point, the method further includes: fitting the processed intraocular pressure data to obtain a fourth intraocular pressure curve; matching the fourth intraocular pressure curve with a preset third type reference intraocular pressure curve and a preset fourth type reference intraocular pressure curve respectively, wherein the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve are obtained by fitting in advance according to the historical intraocular pressure data of corresponding types respectively; and taking the type corresponding to the reference intraocular pressure curve with the highest matching degree as the intraocular pressure type of the target object to be detected.

Optionally, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a third type, acquiring fourth intraocular pressure data of the target object to be detected in the next target time duration; performing difference processing on the fourth intraocular pressure data and intraocular pressure data at a preset time point; fitting the processed intraocular pressure data to obtain a fifth intraocular pressure curve, and acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the fifth intraocular pressure curve respectively; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a third preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

Optionally, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a fourth type, acquiring fifth intraocular pressure data of the target object to be detected in the next target time period; performing difference processing on the fifth intraocular pressure data and intraocular pressure data at a preset time point; fitting the processed intraocular pressure data to obtain a sixth intraocular pressure curve; respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to a fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to a sixth intraocular pressure curve; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a fourth preset value, predicting that the intraocular pressure type of the target object to be detected is a fourth type.

Optionally, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a third type, acquiring seventh intraocular pressure data of the target object to be detected in the next target time period; performing difference processing on the seventh intraocular pressure data and intraocular pressure data at a preset time point; comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types; and when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

Optionally, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising: when the intraocular pressure type of the target object to be detected is a fourth type, acquiring eighth intraocular pressure data of the target object to be detected in the next target time duration; performing difference processing on the eighth intraocular pressure data and intraocular pressure data at a preset time point; comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types; and when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range, predicting the intraocular pressure type of the target object to be detected as the fourth type.

A second aspect of the present invention provides an apparatus for intraocular pressure type detection, comprising: the acquisition module is used for acquiring first ocular pressure data of a target object to be detected; the comparison module is used for comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, and the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type; and the determining module is used for taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected when the first intraocular pressure data falls into the preset first type reference intraocular pressure data range or the preset second type reference intraocular pressure data range.

The functions performed by the components in the intraocular pressure type detection device provided by the present invention have been applied to any of the above method embodiments, and therefore, the details are not repeated herein.

The invention provides computer equipment in a third aspect, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; a memory for storing a computer program; and a processor for implementing the steps of the intraocular pressure type detection method according to the first aspect when executing the program stored in the memory.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the intraocular pressure type detection method as provided in the first aspect of the present invention.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic flow chart of an intraocular pressure type detection method according to an embodiment of the present invention;

FIG. 2 is a graph showing the time-varying rate of intraocular pressure in the ocular hypertension group over 24 hours and its 95% confidence interval, according to an embodiment of the present invention;

fig. 3 is a graph of the 24-hour time-varying intraocular pressure rate of ocular hypertension glaucoma and its 95% confidence interval according to an embodiment of the present invention;

FIG. 4 is a graph showing a comparison of the time varying rate of ocular hypertension versus glaucoma with ocular hypertension for 24 hours, according to one embodiment of the present invention;

fig. 5 is a schematic structural diagram of an eye pressure type detecting device according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a computer device of an eye pressure type detection method according to an embodiment of the present invention.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without inventive step, are within the scope of protection of the disclosure.

Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of the terms "a," "an," or "the" and similar referents in this disclosure also do not denote a limitation of quantity, but rather denote the presence of at least one. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

In view of the technical problems mentioned in the background art, an embodiment of the present invention provides an intraocular pressure type detection method, as shown in fig. 1, the method includes:

step S101, first ocular pressure data of a target object to be detected are obtained. Illustratively, intraocular pressure data of a target object to be detected is acquired, an acquisition mode can be selected according to actual conditions, and a value of the target object at a certain moment, such as intraocular pressure data of working time, can be acquired in a single point; the intraocular pressure data of the target object in the target time period can also be collected, for example, the target time period can be set to 24 hours, and the intraocular pressure monitoring data at 12 time points in 24 hours or 6 time points in the daytime and at night of the patient can be collected. For each time point, multiple consecutive measurements can be made, the average of which is taken as a record. Such as 24 hour intraocular pressure monitoring method: intraocular pressure was measured every 2 hours from 8 am to 6 am, and 12 time points were measured. The intraocular pressure fluctuation information provided by the 24-hour intraocular pressure measurement is more detailed than that of the intraocular pressure measurement at a single time point, and the establishment of a more accurate risk prediction model is facilitated. The specific measurement mode can be adjusted according to actual needs, and is not limited too much here.

And S102, comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, wherein the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type. Illustratively, the historical intraocular pressure database may store corresponding condition data for patients with ocular hypertension and ocular hypertension glaucoma that are authorized by the user. The preset first type reference intraocular pressure data range can be a variation trend range of different points of ocular hypertension, a time variation rate range of ocular hypertension, an ocular hypertension group ocular pressure time variation rate curve and a credible interval thereof; the preset second type reference intraocular pressure data range can be a variation trend range of different points of intraocular pressure of the ocular hypertension glaucoma, a variation rate range of intraocular pressure of the ocular hypertension glaucoma, a variation rate curve of intraocular pressure of the ocular hypertension glaucoma and a credible interval thereof. The first intraocular pressure data is compared with the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range respectively, so that the type of intraocular pressure of the target object is predicted, and auxiliary prediction of eye diseases is facilitated.

And S103, when the first intraocular pressure data falls into a preset first type reference intraocular pressure data range or a preset second type reference intraocular pressure data range, taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected. Illustratively, the target object intraocular pressure data is compared with a preset reference intraocular pressure data range, and a type to which the intraocular pressure of the target object may belong is predicted according to a type of the reference intraocular pressure data range within which the target object intraocular pressure data falls. For example, when the intraocular pressure value falls within a preset first type reference intraocular pressure data range, it can be predicted that the intraocular pressure of the target subject belongs to the first type intraocular pressure. Similarly, it is possible to predict when the target subject's intraocular pressure belongs to the second type of intraocular pressure.

According to the intraocular pressure type detection method provided by the embodiment of the invention, intraocular pressure data of the target object to be detected is obtained, and the response of intraocular pressure fluctuation information provided by the intraocular pressure data to the real state of the target object is more objective. The obtained intraocular pressure data of the target object is compared with the reference intraocular pressure data of the preset type of the system to obtain the type of intraocular pressure abnormality of the target object, so that the diagnosis result differentiation caused by human factors is eliminated, the method is more objective, the accuracy of predicting the intraocular pressure type is improved, and the prediction of corresponding ophthalmic diseases is realized.

As an optional embodiment of the present invention, after step S101, the method includes:

and performing difference processing on the first intraocular pressure data and intraocular pressure data at a preset time point. Illustratively, the intraocular pressure data at the predetermined time point is taken as the selected reference baseline, and a valley or a minimum value of the measured intraocular pressure data of the target subject is generally selected, for example, the intraocular pressure value at 2 am in the collected intraocular pressure data of the target subject is taken as the baseline if the intraocular pressure value at 2 am is the minimum. And subtracting each intraocular pressure data of the acquired target object from the intraocular pressure data of the preset time point to obtain a difference value. Data noise caused by individual difference can be eliminated, and prediction accuracy is improved.

And comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types.

Illustratively, the historical intraocular pressure database may store corresponding condition data for patients with ocular hypertension and ocular hypertension glaucoma that are authorized by the user. The preset third type reference intraocular pressure data range can be a variation trend range of different points of ocular hypertension, a time variation rate range of ocular hypertension, an ocular hypertension group ocular pressure time variation rate curve and a credible interval thereof; the preset fourth type reference intraocular pressure data range can be a variation trend range of different points of the intraocular pressure of the ocular hypertension glaucoma, a variation rate range of the intraocular pressure of the ocular hypertension glaucoma, a variation rate curve of the intraocular pressure of the ocular hypertension glaucoma and a credible interval thereof. The first intraocular pressure data is compared with the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range respectively, so that the type of the intraocular pressure of the target object is predicted, and auxiliary prediction is facilitated.

Intraocular pressure data for a target duration of 24 hours, using a third type of ocular hypertension as an example, are shown in table 1:

TABLE 1 high tension ocular disease 24 hours different time points of intraocular pressure variation trend range

The intraocular pressure data may also include the central corneal thickness, the axis of the eye of the authorized target subject for the predicted relative accuracy, and may also be obtained as to the age and gender of the authorized target subject. The above items are all optional.

In table 1, compared to baseline trough, at the time of arousal (6 am, 00), mean increase in intraocular pressure was greater than 2mmhg, approximately 3mmHg at 00, 12 noon: 00 exceeds 3mmHg; the increase of intraocular pressure slowly decreases in the afternoon, the average increase is maintained at 2mmHg before 8 pm, and the average increase in the morning at 00 o' clock is reduced to about 1 mmHg. At each time point of the awakening period (6 am to 8 pm). The results for the unadjusted model and the adjusted model were similar.

Intraocular pressure data targeted for a 24 hour period, exemplified by the fourth type of hypertensive glaucoma, are shown in table 2:

TABLE 2 high tension glaucoma 24 hours intraocular pressure different time point variation trend range

In table 2, compared to the intraocular pressure trough, at the time of arousal (6 am). Then the average increase of intraocular pressure shows a non-uniform descending trend; the increase of ocular pressure was reduced to 3mmHg at 6 pm, to about 1.5mmHg at 8 pm and 10 pm, and the average increase was maintained at 2mmHg at 0 am. During the period from 6 am to 6 pm at 00, there was a significant statistical difference in intraocular pressure increase compared to baseline (P ≦ 0.001 to P =0.03, respectively). The results of the adjusted model are comparable to the results of the unadjusted model.

And when the processed intraocular pressure data falls into a preset third type reference intraocular pressure data range or a preset fourth type reference intraocular pressure data range, taking the type corresponding to the corresponding parameter intraocular pressure data range as the intraocular pressure type of the target object to be detected. Step S103 in the above embodiments is similar in this embodiment, and is not described again here.

According to the intraocular pressure type detection method provided by the embodiment of the invention, the intraocular pressure data at the preset time point is used as the reference baseline, and the difference is obtained by subtracting each acquired intraocular pressure data of the target object from the intraocular pressure data at the preset time point. The data noise caused by individual difference can be eliminated, the data is more universal and objective, and the prediction accuracy can be improved.

As an optional embodiment of the present invention, the first ocular pressure data comprises intraocular pressure data for a target duration, and the method further comprises:

and fitting the intraocular pressure data of the target duration to obtain a first intraocular pressure curve.

For example, the data fitting may be performed by using a corresponding fitting tool, in this embodiment, a Generalized Additive Model (GAM) is used to perform a smooth curve plotting on the acquired intraocular pressure data of the target object. Because the generalized additive model it allows fitting the model using a non-linear smoothing term without prior knowledge of the relationship between the dependent and independent variables.

And matching the first intraocular pressure curve with a preset first type reference intraocular pressure curve and a preset second type reference intraocular pressure curve respectively, and fitting the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve in advance according to the corresponding types of historical intraocular pressure data respectively to obtain the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve.

Illustratively, the historical intraocular pressure database may store corresponding condition data for patients with ocular hypertension and ocular hypertension glaucoma that are authorized by the user. Presetting a first type reference intraocular pressure curve which can be an intraocular pressure time-varying rate curve of an ocular hypertension group and a credible interval thereof; the preset second type reference intraocular pressure curve can be an intraocular pressure time-varying rate curve of the ocular hypertension glaucoma and a credible interval thereof. The first intraocular pressure data is compared with the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve respectively, so that the type of intraocular pressure of the target object is predicted, and auxiliary prediction is facilitated.

And taking the type corresponding to the reference intraocular pressure curve with the highest matching degree as the intraocular pressure type of the target object to be detected.

Illustratively, when the intraocular pressure time variation curve fitted according to the target object intraocular pressure data falls into the preset first type reference intraocular pressure curve confidence interval in more parts than the preset second type reference intraocular pressure curve confidence interval, the target object intraocular pressure time variation curve is considered to have a higher matching degree with the preset first type reference intraocular pressure curve, and the target object intraocular pressure can be preliminarily judged to be the first type. Similarly, it may be determined initially when the target subject intraocular pressure is of the second type.

When the first type reference intraocular pressure curve is preset as an intraocular pressure time varying rate curve of an ocular hypertension group and a credible interval thereof, and the second type reference intraocular pressure curve is preset as an intraocular pressure time varying rate curve of ocular hypertension glaucoma and a credible interval thereof. When the part of the intraocular pressure time-varying curve fitted according to the target object intraocular pressure data, which falls into the intraocular pressure time-varying rate curve of the ocular hypertension group and the credible interval thereof, is more than the part of the intraocular pressure time-varying rate curve of the ocular hypertension glaucoma and the credible interval thereof, the matching degree of the target object intraocular pressure time-varying curve with the intraocular pressure time-varying rate curve of the ocular hypertension group and the credible interval thereof is considered to be higher, and the intraocular pressure of the target object can be preliminarily judged to be the intraocular pressure of the ocular hypertension type. Similarly, it can be preliminarily determined when the target intraocular pressure is of the hypertensive glaucoma type.

According to the intraocular pressure type detection method provided by the embodiment of the invention, the acquired intraocular pressure data of the target object is fit to a smooth curve, so that on one hand, the intraocular pressure change trend of the target object can be reflected more objectively by enough data volume; on the other hand, data with large noise can be effectively removed, and the accuracy of prediction is improved.

As an optional embodiment of the present invention, the first type of corresponding symptom is a prophase symptom of the second type of corresponding symptom, and the method comprises:

and when the intraocular pressure type of the target object to be detected is the first type, acquiring second intraocular pressure data of the target object to be detected in the next target time period.

Illustratively, the first type is ocular hypertension, since ocular hypertension is a pre-symptom of ocular hypertension glaucoma. So in order to assess the progression of ocular hypertension in a target subject, the target subject is reviewed after a corresponding time has elapsed. If a certain ocular hypertension patient obtains 24-hour intraocular pressure data for n times through reexamination, the first time is a baseline, the later n times are reexamination data for n times, two 11 time point valley value differences can be obtained by reexamination data, and n 11 time point valley value differences can be obtained by n times of follow-up.

And performing fitting operation on the second intraocular pressure data to obtain a second intraocular pressure curve.

The ascending time-varying rate and the descending time-varying rate corresponding to the first intraocular pressure curve and the ascending time-varying rate and the descending time-varying rate corresponding to the second intraocular pressure curve are respectively obtained.

Illustratively, a smooth curve fitted via a generalized additive model has a corresponding time-varying rate in its ascending portion and a corresponding time-varying rate in its descending portion. For example, the time-varying rate of rise corresponding to the first intraocular pressure curve is taken to be a, and the time-varying rate of rise corresponding to the second intraocular pressure curve is taken to be B. The same way the corresponding time varying rate of the falling part can be obtained.

And if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than the first preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a second type.

Illustratively, when the ascending time variation rate of the first type reference intraocular pressure curve is preset to be C, the ascending time variation rate of the second type reference intraocular pressure curve is preset to be D, and the first preset value is K, then K = D-C. And when E is larger than K, predicting that the intraocular pressure type of the target object to be detected is changed from the first type to the second type. When the first type is ocular hypertension and the second type is ocular hypertension glaucoma, the intraocular pressure type of the target object to be detected can be predicted to be changed from ocular hypertension type to ocular hypertension glaucoma type when the above conditions are satisfied.

As an alternative embodiment of the present invention, the first type of corresponding symptom is a prophase symptom of the second type of corresponding symptom, and the method comprises:

and when the intraocular pressure type of the target object to be detected is the second type, acquiring third intraocular pressure data of the target object to be detected in the next target time period. And performing fitting operation on the third intraocular pressure data to obtain a third intraocular pressure curve. The time-varying rate of ascent and descent corresponding to the first intraocular pressure profile and the time-varying rate of ascent and descent corresponding to the third intraocular pressure profile are acquired, respectively. For details, refer to the description of the corresponding steps in the above embodiments, which are not repeated herein.

And if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a second preset value, predicting the intraocular pressure type of the target object to be detected to be a second type.

Illustratively, when the first type is ocular hypertension and the second type is ocular hypertension glaucoma, the ocular tension type of the target object to be detected can be predicted to be ocular hypertension glaucoma type when the above conditions are satisfied. It should be noted that the second preset value in this embodiment may be the same as the first preset value, or different preset values may be set according to actual specific data.

The intraocular pressure type detection method provided by the embodiment of the invention provides a method for judging whether hypertensive glaucoma of a target object progresses. The characteristic of the time-varying curve of the ocular hypertension and the time-varying curve of the glaucoma with the ocular hypertension are clinically researched and applied to clinical auxiliary prediction, and the appropriate migration of the time-varying curve of the ocular hypertension and the glaucoma is applied to prediction of the progression of the ocular hypertension glaucoma. On one hand, the prediction is more accurate based on the support of scientific theory; on the other hand, the method is beneficial to pertinence early intervention and avoids adverse consequences.

As an optional embodiment of the present invention, the first intraocular pressure data includes intraocular pressure data of a target duration, and after the step of performing difference processing on the first intraocular pressure data and intraocular pressure data at a preset time point, the method further includes:

and fitting the processed intraocular pressure data to obtain a fourth intraocular pressure curve.

And matching the fourth intraocular pressure curve with a preset third type reference intraocular pressure curve and a preset fourth type reference intraocular pressure curve respectively, and fitting the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve in advance according to the corresponding types of historical intraocular pressure data respectively to obtain the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve.

Illustratively, the historical intraocular pressure database may store corresponding condition data for patients with ocular hypertension and ocular hypertension glaucoma that are authorized by the user. The preset third type reference intraocular pressure curve can be an intraocular pressure time-varying rate curve of an ocular hypertension group and a credible interval thereof; the preset fourth type reference intraocular pressure curve can be an intraocular pressure time change rate curve of the ocular hypertension glaucoma and a credible interval thereof. And comparing the fourth intraocular pressure data with the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve respectively, so as to predict the type of the intraocular pressure of the target object, and facilitate auxiliary prediction.

Illustratively, when a third type of reference intraocular pressure curve is preset as a 24-hour intraocular pressure time-varying rate curve of an ocular hypertension group and its 95% confidence interval, as shown in fig. 2, with a intraocular pressure valley at 2 a.m. as a baseline, the solid line in this embodiment represents the time-varying rate curve fitted from the historical intraocular pressure database, and the area between the dotted lines represents the 95% confidence interval. In practice, the size of the trusted interval may be adjusted according to the size of the data volume, and is not limited here. Overall, the average increase of the intraocular pressure of the patients with ocular hypertension shows a slow trend of rising first and falling second. The specific information of fig. 2 is shown in table 3:

TABLE 3 The 24-hour time-to-intraocular pressure rate ranges for ocular hypertension

The 24-hour whole-day trend was from 2 am to 10 am at the trough, intraocular pressure positively correlated with time, and intraocular pressure increased by 0.40mmHg for each one unit (2 hours) of time increase (95-th-ci; after 10 am, intraocular pressure was time-inversely correlated, with intraocular pressure reductions of 0.17mmHg for every 2 hours of increase (1 unit in 2 hours) (95% CI: -0.26, -0.08 p-Ap <0.0001). The results for the unadjusted model and the adjusted model were similar.

When the fourth type reference intraocular pressure curve is preset as a 24-hour intraocular pressure time-varying rate curve of ocular hypertension glaucoma and a 95% confidence interval thereof, as shown in fig. 3, with a intraocular pressure valley at 2 am as a baseline, the solid line in this embodiment represents the time-varying rate curve obtained by fitting according to the historical intraocular pressure database, and the area between the dotted lines represents the 95% confidence interval. In practice, the size of the trusted interval may be adjusted according to the size of the data volume, and is not limited here. Overall, the average increase of the intraocular pressure of the high-tension glaucoma patient shows a remarkable rising trend. The specific information of fig. 3 is shown in table 4:

TABLE 4 24-hour time-varying rate range for ocular hypertension glaucoma

A 24-hour whole day trend from 2 am to 10 am with positive correlation between intraocular pressure and time, with ocular pressure increase in the hypertensive glaucoma group of 0.69mmHg for each unit increase in time (2 hours) (95% ci; after 10 AM, the intraocular pressure was negatively correlated with time, and for each increase in time of one unit (2 hours), the intraocular pressure was reduced by 0.31mmHg (95% CI: -0.41, -0.20P-bags 0.0001). The calculation comparison of the unadjusted model and the adjusted model can be carried out, and the research results are basically equivalent.

When the intraocular pressure time-varying curve falls into the credible interval of the graph in fig. 2, the patient with the ocular hypertension can be preliminarily judged; data when the intraocular pressure time-varying curve falls within the confidence interval of fig. 3 can be preliminarily determined as a patient with ocular hypertension glaucoma.

According to the intraocular pressure type detection method provided by the embodiment of the invention, the acquired intraocular pressure data of the target object is subjected to difference processing, so that data noise caused by individual difference can be eliminated, the data is more universal and objective, and the data noise caused by the individual difference can be eliminated. Meanwhile, the data after difference processing is fitted into a smooth curve, so that on one hand, the change trend of the intraocular pressure of the target object can be reflected more objectively by enough data amount of the data; on the other hand, data with large noise can be effectively removed, and the accuracy of prediction is improved.

As an alternative embodiment of the present invention, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, and the method includes:

when the intraocular pressure type of the target object to be detected is a third type, acquiring fourth intraocular pressure data of the target object to be detected in the next target time duration; performing difference processing on the fourth intraocular pressure data and intraocular pressure data at a preset time point; fitting the processed intraocular pressure data to obtain a fifth intraocular pressure curve; respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to a fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to a fifth intraocular pressure curve; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a third preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

Illustratively, as shown in fig. 4, a Generalized Additive Mixed Models (GAMM) model is used to graphically represent the progression risks of different time point variation trends and time varying rate trends and to quantitatively evaluate the progression risks. The generalized additive mixture model is a combination of a generalized additive model in which a smooth curve fitting term can be specified and a mixture model in which a random effect (which may be a random intercept or/and a random time slope) can be introduced. The method is mainly used for analyzing repeated measurement data, eliminates the influence of individual difference on the results of repeated measurement by introducing random effect, and improves the statistical efficiency. Ocular hypertension was compared to 24-hour time-varying intraocular pressure rate curves for ocular hypertension glaucoma. In both ocular hypertension and glaucoma, the increase in intraocular pressure follows an inverted U-shaped curve with a trough at 2:00 hours (set as baseline), inflection point was approximately 10:00. from 2 am to 10 am, intraocular pressure was positively correlated with time. After 10 am, intraocular pressure was negatively correlated with time. The increase and decrease in the inverted U-shaped curve in the ocular hypertension glaucoma group were steeper than in the ocular hypertension group, and the increase and decrease in ocular pressure in the ocular hypertension glaucoma group were increased by 0.30mmHg per 2-hour increase than in the ocular hypertension group (95% CI. The specific differences are shown in table 5:

TABLE 5 differential time-varying rates of ocular hypertension and ocular hypertension glaucoma over 24 hours

The risk of progression of 24-hour all-day trend was based on intraocular pressure at 2 am, and from 2 am to 10 am, time was increased by 0.30mmHg more than the non-progression increase in intraocular pressure every time increased by one unit (2 hours) (95% CI; after 10 am, for each increase in time of one unit (2 hours), the eye pressure drop amplitude of the progressive group decreased by 0.14mmHg more than that of the non-progressive group (95%. The results for the unadjusted model and the adjusted model were similar.

Illustratively, 24-hour intraocular pressure data of a target object acquired for the first time and 24-hour intraocular pressure data of a target object acquired for subsequent follow-up or review are respectively subjected to smooth curve drawing by using a generalized addition model, and then curve fitting is performed by using a generalized addition mixed model, so as to obtain a calculation coefficient, namely an ascending time variation rate or a descending time variation rate, such as β in fig. 4, and a difference value corresponding to the ascending time variation rate or a difference value corresponding to the descending time variation rate, such as Δ β in fig. 4. And then comparing the obtained calculation coefficient with a preset value. For example, as can be seen from table 5, when the difference value corresponding to the shift rate is 0.3 at the time of data increase in the database, and the target object measured value is 0.3 or more, it is reasonable to predict that the intraocular pressure type of the target object has shifted. Similarly, the difference corresponding to the rate of change in the fall time may be compared. It should be noted that the third preset value in this embodiment may be the same as the first preset value, or different preset values may be set according to actual specific data.

As an alternative embodiment of the present invention, the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, and the method further comprises:

when the intraocular pressure type of the target object to be detected is a fourth type, acquiring fifth intraocular pressure data of the target object to be detected in the next target time period; performing difference processing on the fifth intraocular pressure data and intraocular pressure data at a preset time point; fitting the processed intraocular pressure data to obtain a sixth intraocular pressure curve; respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to a fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to a sixth intraocular pressure curve; and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a fourth preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type. In this embodiment, the description of the corresponding steps refers to the above embodiments, and is not repeated herein. It should be noted that the fourth preset value in this embodiment may be the same as the second preset value, or different preset values may be set according to actual specific data.

The intraocular pressure type detection method provided by the embodiment of the invention provides a method for judging whether the hypertensive glaucoma of the eye of the target object progresses. The characteristic of the time-varying curve of the ocular hypertension and the time-varying curve of the glaucoma with the ocular hypertension are clinically researched and applied to clinical auxiliary prediction, and the appropriate migration of the time-varying curve of the ocular hypertension and the glaucoma is applied to prediction of the progression of the ocular hypertension glaucoma. On one hand, the prediction is more accurate based on the support of scientific theory; on the other hand, the method is beneficial to pertinently intervene in advance and avoids adverse consequences.

when the intraocular pressure type of the target object to be detected is a third type, acquiring seventh intraocular pressure data of the target object to be detected in the next target time period; performing difference processing on the seventh intraocular pressure data and intraocular pressure data at a preset time point; comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types; and when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

Illustratively, the scheme predicts the intraocular pressure type progress of the target object by comparing the collected review intraocular pressure data of the target object with a discrete reference intraocular pressure data range. For example, when the target valley intraocular pressure is collected for the first time, and the variation interval at 10 points of the patient with ocular hypertension is 2.97 (1.53, 4.41) referring to table 1, the predicted value at this point is 20.97 (19.53, 22.41), and the variation interval at 10 points of the patient with ocular hypertension glaucoma is 5.01 (3.06, 6.96) referring to table 2, the predicted value at this point is 23.01 (21.06, 24.96). Predicting the intraocular pressure of the type of ocular hypertension when the measured value of 10 points of the target object is 20 mmHg; in the later continuous observation, the measured value of 10 points of the target object is 24mmHg, and the intraocular pressure of the high-intraocular-pressure glaucoma type is predicted. It is predicted that the type of ocular pressure of the target subject changes from ocular hypertension type to ocular hypertension glaucoma type.

Illustratively, when the measured value at 10 points of the patient is 25mmHg, the intraocular pressure can be predicted to be that of the ocular hypertension glaucoma type; the patient found 22mmH, highly suspected ocular pressure of hypertensive glaucoma type, closely followed; the patient had 21mmHg intraocular pressure, which may be the lower limit of the hypertensive glaucoma interval, with some risk, and continued observation. The later prediction mode is the same. It should be noted that the present embodiment takes 10 o' clock as an example, and theoretically any time point can be used, which is not limited herein.

The intraocular pressure type detection method provided by the embodiment of the invention provides a method for judging whether an ocular hypertension group is developed into an ocular hypertension glaucoma group or not through different discrete time point variation trend ranges of intraocular pressure abnormality in historical research data. The variation range of the ocular hypertension at different time points and the variation range of the ocular hypertension glaucoma at different time points are researched clinically. The intraocular pressure data collected to the target object is compared with intraocular pressure data ranges of different preset types, and the progress condition of intraocular pressure abnormity is judged through the range of the comparative data. On one hand, the prediction is more accurate based on the support of scientific theory; on the other hand, the method can perform targeted intervention in advance, so as to avoid adverse consequences.

when the intraocular pressure type of the target object to be detected is a fourth type, acquiring eighth intraocular pressure data of the target object to be detected in the next target time period; performing difference processing on the eighth intraocular pressure data and intraocular pressure data at a preset time point; comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types; and when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range, predicting the intraocular pressure type of the target object to be detected to be a fourth type.

The intraocular pressure type detection method provided by the embodiment of the invention provides a method for judging whether an ocular hypertension group is developed into an ocular hypertension glaucoma group or not through different discrete time point variation trend ranges of intraocular pressure abnormality in historical research data. The variation range of the ocular hypertension at different time points and the variation range of the ocular hypertension glaucoma at different time points are researched clinically. And comparing the intraocular pressure data acquired to the target object with preset intraocular pressure data ranges of different types, and judging the progress condition of intraocular pressure abnormality through the range of the comparative data. On one hand, the prediction is more accurate based on the support of scientific theory; on the other hand, the method can perform targeted intervention in advance, so as to avoid adverse consequences.

Fig. 5 is a device for detecting an intraocular pressure type according to an embodiment of the present invention, where the intraocular pressure type detecting device in this embodiment includes:

the obtaining module 510 is configured to obtain first eye pressure data of a target object to be detected. For details, refer to the description of step S101 in the above embodiment, and are not repeated herein.

The comparison module 520 is configured to compare the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range, where the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type. For details, refer to the description of step S102 in the above embodiment, and are not repeated herein.

The determining module 530 is configured to, when the first intraocular pressure data falls within a preset first-type reference intraocular pressure data range or a preset second-type reference intraocular pressure data range, take a type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected. For details, refer to the description of step S103 in the above embodiment, which is not repeated herein.

As an optional implementation manner of the present invention, the intraocular pressure type detecting device in this embodiment further includes:

and the first differencing unit is used for differencing the first intraocular pressure data and intraocular pressure data at a preset time point.

And the first comparison unit is used for comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, and the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding types.

And the first determining unit is used for taking the type corresponding to the corresponding parameter intraocular pressure data range as the intraocular pressure type of the target object to be detected when the processed intraocular pressure data falls into a preset third type reference intraocular pressure data range or a preset fourth type reference intraocular pressure data range.

the first fitting unit is used for performing fitting operation on the intraocular pressure data of the target duration to obtain a first intraocular pressure curve.

And the first matching unit is used for matching the first intraocular pressure curve with a preset first type reference intraocular pressure curve and a preset second type reference intraocular pressure curve respectively, and the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve are obtained by fitting in advance according to the corresponding types of historical intraocular pressure data respectively.

And the second determination unit is used for taking the type corresponding to the reference intraocular pressure curve with the highest matching degree as the intraocular pressure type of the target object to be detected.

the first acquiring unit is used for acquiring second intraocular pressure data of the target object to be detected in the next target time period when the intraocular pressure type of the target object to be detected is the first type.

And the second fitting unit is used for performing fitting operation on the second intraocular pressure data to obtain a second intraocular pressure curve.

And the second acquisition unit is used for acquiring the ascending time-varying rate and the descending time-varying rate corresponding to the first intraocular pressure curve and the ascending time-varying rate and the descending time-varying rate corresponding to the second intraocular pressure curve respectively.

And the first prediction unit is used for predicting that the intraocular pressure type of the target object to be detected is converted into the second type if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is greater than the first preset value.

and the third acquisition unit is used for acquiring third intraocular pressure data of the target object to be detected in the next target time length when the intraocular pressure type of the target object to be detected is the second type.

And the third fitting unit is used for performing fitting operation on the third intraocular pressure data to obtain a third intraocular pressure curve.

And the fourth acquisition unit is used for respectively acquiring the ascending time-varying rate and the descending time-varying rate corresponding to the first intraocular pressure curve and the ascending time-varying rate and the descending time-varying rate corresponding to the third intraocular pressure curve.

And the second prediction unit is used for predicting the intraocular pressure type of the target object to be detected to be a second type if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is greater than a second preset value.

and the fourth fitting unit is used for performing fitting operation on the processed intraocular pressure data to obtain a fourth intraocular pressure curve.

And the second matching unit is used for matching the fourth intraocular pressure curve with a preset third type reference intraocular pressure curve and a preset fourth type reference intraocular pressure curve respectively, and the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve are obtained by fitting in advance according to the corresponding types of historical intraocular pressure data respectively.

And the third determining unit is used for taking the type corresponding to the reference intraocular pressure curve with the highest matching degree as the intraocular pressure type of the target object to be detected.

and the fifth acquiring unit is used for acquiring fourth eye pressure data of the target object to be detected in the next target time length when the eye pressure type of the target object to be detected is the third type.

And the second differencing unit is used for differencing the fourth ocular pressure data and the intraocular pressure data at the preset time point.

And the fifth fitting unit is used for performing fitting operation on the processed intraocular pressure data to obtain a fifth intraocular pressure curve.

And a sixth acquiring unit, configured to acquire the ascending time-varying rate and the descending time-varying rate corresponding to the fourth intraocular pressure curve and the ascending time-varying rate and the descending time-varying rate corresponding to the fifth intraocular pressure curve, respectively.

And the third prediction unit is used for predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is greater than a third preset value.

and the seventh acquiring unit is used for acquiring fifth intraocular pressure data of the target object to be detected in the next target time period when the intraocular pressure type of the target object to be detected is the fourth type.

And the third differencing unit is used for differencing the fifth intraocular pressure data and the intraocular pressure data at the preset time point.

And the sixth fitting unit is used for performing fitting operation on the processed intraocular pressure data to obtain a sixth intraocular pressure curve.

And an eighth acquiring unit, configured to acquire the ascending time-varying rate and the descending time-varying rate corresponding to the fourth intraocular pressure curve and the ascending time-varying rate and the descending time-varying rate corresponding to the sixth intraocular pressure curve, respectively.

And the fourth prediction unit is used for predicting the intraocular pressure type of the target object to be detected to be a fourth type if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is greater than a fourth preset value.

and the ninth acquiring unit is used for acquiring seventh intraocular pressure data of the target object to be detected in the next target time period when the intraocular pressure type of the target object to be detected is the third type.

And the fourth differencing unit is used for differencing the seventh intraocular pressure data with the intraocular pressure data at the preset time point.

And the second comparison unit is used for comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, and the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding types.

And the fifth prediction unit is used for predicting that the intraocular pressure type of the target object to be detected is converted into the fourth type when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range.

and the tenth acquiring unit is used for acquiring eighth intraocular pressure data of the target object to be detected in the next target time period when the intraocular pressure type of the target object to be detected is the fourth type.

And a fifth differencing unit for differencing the eighth intraocular pressure data with the intraocular pressure data at the preset time point.

And the third comparison unit is used for comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, and the preset third type reference intraocular pressure data range and the preset fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding types.

And the sixth prediction unit is used for predicting the intraocular pressure type of the target object to be detected to be the fourth type when the processed intraocular pressure data falls into a preset fourth type reference intraocular pressure data range.

An embodiment of the present invention provides a computer device, as shown in fig. 6, the device includes one or more processors 610 and a storage 620, where the storage 620 includes a persistent memory, a volatile memory, and a hard disk, and one processor 610 is taken as an example in fig. 6. The apparatus may further include: an input device 630 and an output device 640.

The processor 610, the memory 620, the input device 630, and the output device 640 may be connected by a bus or other means, such as the bus connection in fig. 6.

Processor 610 may be a Central Processing Unit (CPU). The Processor 610 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the intraocular pressure type detection device, and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 620 optionally includes memory located remotely from processor 610, which may be connected to the tonality detection apparatus via a network. Input device 630 may receive user input of a calculation request (or other numeric or alphanumeric information) and generate key signal inputs associated with an intraocular pressure type detection device. The output device 640 may include a display device such as a display screen for outputting the calculation result.

Embodiments of the present invention provide a computer-readable storage medium storing computer instructions, the computer-readable storage medium storing computer-executable instructions, the computer-executable instructions being capable of performing an intraocular pressure type detection method in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or flash Memory), an optical fiber device, and a portable Compact Disc Read-Only Memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

In the description of the present specification, reference to the description of the terms "this embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.

The above description is only for the purpose of illustrating the preferred embodiments of the present disclosure and is not to be construed as limiting the present disclosure, but rather as the following description is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.

Claims

1. An intraocular pressure type detection method, comprising:

acquiring first eye pressure data of a target object to be detected;

comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, wherein the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type;

and when the first intraocular pressure data falls into the preset first type reference intraocular pressure data range or the preset second type reference intraocular pressure data range, taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected.

2. The intraocular pressure type detection method according to claim 1, wherein after the obtaining of the first intraocular pressure data of the target object to be detected, the method further comprises:

performing difference processing on the first intraocular pressure data and intraocular pressure data at a preset time point;

comparing the processed intraocular pressure data with a preset third type reference intraocular pressure data range and a preset fourth type reference intraocular pressure data range respectively, wherein the preset third type reference intraocular pressure data range and the fourth type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of corresponding types;

and when the processed intraocular pressure data falls into the preset third type reference intraocular pressure data range or the preset fourth type reference intraocular pressure data range, taking the type corresponding to the corresponding parameter intraocular pressure data range as the intraocular pressure type of the target object to be detected.

3. The tonometric detection method of claim 1, wherein said first tonometric data comprises tonometric data for a target duration, said method further comprising:

fitting the intraocular pressure data of the target duration to obtain a first intraocular pressure curve;

matching the first intraocular pressure curve with the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve respectively, wherein the preset first type reference intraocular pressure curve and the preset second type reference intraocular pressure curve are obtained by fitting in advance according to the historical intraocular pressure data of corresponding types respectively;

4. The intraocular pressure type detection method according to claim 3, wherein the first type of corresponding symptom is a prophase symptom of the second type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a first type, acquiring second intraocular pressure data of the target object to be detected in the next target time period;

fitting the second intraocular pressure data to obtain a second intraocular pressure curve;

respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the first intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the second intraocular pressure curve;

and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a first preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a second type.

5. The intraocular pressure type detection method according to claim 3, wherein the first type of corresponding symptom is a prophase symptom of the second type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a second type, acquiring third intraocular pressure data of the target object to be detected in the next target time period;

fitting the third intraocular pressure data to obtain a third intraocular pressure curve;

respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the first intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the third intraocular pressure curve;

and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a second preset value, predicting that the intraocular pressure type of the target object to be detected is a second type.

6. The intraocular pressure type detection method according to claim 2, wherein the first intraocular pressure data includes intraocular pressure data of a target duration, and after the step of differencing the first intraocular pressure data with intraocular pressure data at a preset time point, the method further includes:

fitting the processed intraocular pressure data to obtain a fourth intraocular pressure curve;

matching the fourth intraocular pressure curve with the preset third type reference intraocular pressure curve and the preset fourth type reference intraocular pressure curve respectively, wherein the preset third type reference intraocular pressure curve and the fourth type reference intraocular pressure curve are obtained by pre-fitting according to the historical intraocular pressure data of corresponding types respectively;

7. The intraocular pressure type detection method according to claim 6, wherein the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a third type, acquiring fourth intraocular pressure data of the target object to be detected in the next target time duration;

performing difference processing on the fourth intraocular pressure data and intraocular pressure data at a preset time point;

fitting the processed intraocular pressure data to obtain a fifth intraocular pressure curve;

respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the fifth intraocular pressure curve;

and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a third preset value, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

8. The method for detecting intraocular pressure type according to claim 6, wherein the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a fourth type, acquiring fifth intraocular pressure data of the target object to be detected in the next target time period;

performing difference processing on the fifth intraocular pressure data and intraocular pressure data at a preset time point;

fitting the processed intraocular pressure data to obtain a sixth intraocular pressure curve;

respectively acquiring an ascending time-varying rate and a descending time-varying rate corresponding to the fourth intraocular pressure curve and an ascending time-varying rate and a descending time-varying rate corresponding to the sixth intraocular pressure curve;

and if the difference value corresponding to the ascending time-varying rate or the difference value corresponding to the descending time-varying rate is larger than a fourth preset value, predicting that the intraocular pressure type of the target object to be detected is a fourth type.

9. The method for detecting intraocular pressure type according to claim 2, wherein the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a third type, acquiring seventh intraocular pressure data of the target object to be detected in the next target time period;

performing difference processing on the seventh intraocular pressure data and intraocular pressure data at a preset time point;

and when the processed intraocular pressure data falls into the preset fourth type reference intraocular pressure data range, predicting that the intraocular pressure type of the target object to be detected is converted into a fourth type.

10. The method for detecting intraocular pressure type according to claim 2, wherein the third type of corresponding symptom is a prophase symptom of the fourth type of corresponding symptom, the method further comprising:

when the intraocular pressure type of the target object to be detected is a fourth type, acquiring eighth intraocular pressure data of the target object to be detected in the next target time period;

performing difference processing on the eighth intraocular pressure data and intraocular pressure data at a preset time point;

and when the processed intraocular pressure data falls into the preset fourth type reference intraocular pressure data range, predicting the intraocular pressure type of the target object to be detected to be a fourth type.

11. An apparatus for intraocular pressure type detection, comprising:

the acquisition module is used for acquiring first eye pressure data of a target object to be detected;

the comparison module is used for comparing the first intraocular pressure data with a preset first type reference intraocular pressure data range and a preset second type reference intraocular pressure data range respectively, and the preset first type reference intraocular pressure data range and the preset second type reference intraocular pressure data range are determined according to the acquired historical intraocular pressure data of the corresponding type;

and the determining module is used for taking the type corresponding to the corresponding reference intraocular pressure data range as the intraocular pressure type of the target object to be detected when the first intraocular pressure data falls into the preset first type reference intraocular pressure data range or the preset second type reference intraocular pressure data range.

12. The computer equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the communication bus;

a memory for storing a computer program;

a processor for implementing the steps of the intraocular pressure type detection method according to any one of claims 1 to 10 when executing a program stored in the memory.

13. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the tonality detection method according to any one of claims 1-10.