CN113425319A - Respiratory system disease intelligent auxiliary diagnosis decision-making method for basic medical institution - Google Patents
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 18
- 208000023504 respiratory system disease Diseases 0.000 title claims abstract description 14
- 238000006213 oxygenation reaction Methods 0.000 claims abstract description 35
- 208000019693 Lung disease Diseases 0.000 claims abstract description 18
- 238000009423 ventilation Methods 0.000 claims abstract description 16
- 230000003902 lesion Effects 0.000 claims abstract description 12
- 208000011623 Obstructive Lung disease Diseases 0.000 claims abstract description 11
- 230000011218 segmentation Effects 0.000 claims abstract description 10
- 208000006673 asthma Diseases 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 201000010099 disease Diseases 0.000 claims abstract description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 13
- 239000001301 oxygen Substances 0.000 claims description 13
- 229910052760 oxygen Inorganic materials 0.000 claims description 13
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 10
- 206010028980 Neoplasm Diseases 0.000 claims description 7
- 238000003709 image segmentation Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 abstract description 6
- 208000037841 lung tumor Diseases 0.000 abstract description 2
- 210000004072 lung Anatomy 0.000 description 6
- 206010035664 Pneumonia Diseases 0.000 description 5
- 241000519995 Stachys sylvatica Species 0.000 description 4
- 239000000823 artificial membrane Substances 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 230000009545 invasion Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002512 chemotherapy Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 208000020816 lung neoplasm Diseases 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
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- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
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Abstract
The invention relates to the technical field of assistance in disease diagnosis, in particular to an intelligent assistance diagnosis decision-making method for respiratory system diseases of a primary medical institution; the method comprises the steps of image acquisition; converting the image into a digital image and importing the digital image into a computer; carrying out image preprocessing, image enhancement and lesion area segmentation by adopting an algorithm; extracting the characteristics of the lesion area by adopting an algorithm; making a judgment according to the characteristic data; and dynamically managing and controlling the mechanical auxiliary ventilation according to the oxygenation index. The auxiliary doctor can carry out imaging rapid diagnosis on a large number of lung disease patients, can rapidly distinguish patients with obstructive lung diseases, tumors and asthma, and strives for precious time for patients with obstructive lung diseases.
Description
Technical Field
The invention relates to the technical field of assistance in disease diagnosis, in particular to an intelligent assistance diagnosis decision-making method for respiratory system diseases of a primary medical institution.
Background
In the conventional respiratory diseases, the imaging technology is widely used, for example, lung tumor, asthma, pneumonia, etc. are used in the imaging technology such as CT, X-ray, etc. The lung diseases are divided into three types which have great differences in treatment means, wherein the first type is tumors, and operations or chemotherapy are needed after diagnosis; the second is asthma, which needs to be treated with drugs after diagnosis; the third is obstructive pulmonary disease, such as new coronary pneumonia occurring in 2020, in more serious pulmonary diseases, oxygenation index is lower (lower than 300mmhg) due to lung lesion, and at this time, mechanical auxiliary means such as oxygen inhalation, a breathing machine and even artificial membrane lung are needed to rescue patients. The prior art has the defects that all image materials need to be manually checked, and then further examination items are developed according to disease types possibly corresponding to the characteristics of the images, so that the classification time is long. Due to the long diagnostic time, in the case of obstructive pulmonary disease, there is a possibility that the patient's oxygenation index will be low but not necessarily perceptible by the patient himself, and that deterioration may occur while waiting.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for assisting in accelerating the rapid classification of lung disease images and providing a preset scheme.
The technical scheme of the invention is as follows:
the respiratory system disease intelligent auxiliary diagnosis decision-making method facing the primary medical institution is characterized in that: it comprises the following steps:
1) collecting an image;
2) converting the image into a digital image and importing the digital image into a computer;
3) carrying out image preprocessing, image enhancement and lesion area segmentation by adopting an algorithm;
4) extracting the characteristics of the lesion area by adopting an algorithm;
5) making a judgment according to the characteristic data obtained in the step 4, and if the judgment result is one of tumor and asthma, issuing the result to a decision maker; if the judgment result is the obstructive pulmonary disease, sending an oxygenation index detection request to a decision maker and synchronously starting mechanical assisted ventilation (oxygen inhalation or a breathing machine); as can be easily understood, the starting of mechanical assisted ventilation inevitably affects the oxygenation index, but as long as the decision maker detects the oxygenation index in a shorter time, the influence caused by ventilation is smaller, so that the final correct diagnosis cannot be substantially affected, and the starting of mechanical assisted ventilation in advance can reduce the substantial damage of a patient caused by the decrease of the oxygenation index;
6) and dynamically managing and controlling the mechanical auxiliary ventilation according to the oxygenation index.
In the step 1, the image is acquired by adopting a CT flat scanning mode, the advantage is that the image imaging is fast, the image can be obtained by scanning, and part of CT equipment has an electronic data transmission function and can directly transmit the image to a computer, so that the step 2 is omitted.
In step 3, the image preprocessing and image enhancement include: a) denoising the input image to obtain a denoised image; b) carrying out edge extraction on the denoised image to obtain an edge image; c) carrying out image enhancement processing on the edge image to obtain a denoised edge-enhanced image; d) processing the denoised image by using a brightness-controllable histogram equalization method to obtain a global enhanced image; e) linearly superposing the images obtained in the step c and the step d to obtain a final output image; the lesion region segmentation comprises f) detecting an initial contour of the object of interest from the image; g) classifying points on the initial contour according to the contrast characteristic; h) constructing different energy function models for different kinds of points, calculating an evolution function of a curve for image segmentation based on the constructed energy function models, and moving a level set curve in an image according to the evolution function; i) and determining whether the shifted level set curve has converged, outputting the shifted level set curve as a result of image segmentation if the shifted level set curve is determined to have converged, and performing the steps g) and h) again with the shifted level set curve as an initial contour if the shifted level set curve is determined not to have converged until a converged level set curve is obtained.
In step 4, the extracting of the features includes: firstly, reducing a target image obtained by segmentation into pictures with the size of 16 × 16, wherein the total number of the pictures is 256 pixels; secondly, converting the reduced picture into 256-level gray; thirdly, calculating the gray values of 256 pixel points; fourthly, calculating an average gray value; comparing the gray value of each pixel point with the average gray value, wherein the gray value is greater than or equal to the record 1 of the average value and is less than the record 0 of the average value; and sixthly, combining the comparison results of the step five together to form a 256-bit integer, wherein the integer is the feature code of the picture.
In step 5, the historical images of the three types of lung diseases are processed in steps 1-4 and then respectively recorded in feature databases of the three types of lung diseases, and then the images of the lung diseases to be judged are processed in steps 1-4 and then subjected to a k-nearest neighbor algorithm with feature codes in the feature databases to judge which classification the input data belongs to. Because the images of the three types of lung diseases are very different, no white spots exist in the images of asthma, the white spots of tumors are generally in a shape of a lump and have strong continuity, and have burr edges or smooth edges, and the obstructive pulmonary diseases (mainly pneumonia) are generally in a shape of frosty glass, the three types of lung diseases can be classified efficiently through the algorithm, and the work intensity of a doctor for seeing the lung is reduced.
In the step 5, the oxygenation index is detected by using a blood gas analyzer, and the oxygenation index is tested by using an electrode attaching mode, so that continuous detection and non-invasion can be realized.
Wherein, in step 6, the dynamic management and control includes: setting the oxygenation index range of oxygen supply, starting oxygen inhalation early warning when the real-time oxygenation index is in the range, starting a breathing machine early warning when the oxygenation index still does not rise after the oxygen inhalation is started for 20 seconds, starting a danger early warning when the oxygenation index still does not rise after the breathing machine is started for 30 seconds, and enabling medical personnel to take artificial membrane lung or other severe treatment means according to on-site medical conditions.
The invention has the beneficial effects that: the auxiliary doctor can carry out imaging rapid diagnosis on a large number of lung disease patients, and can rapidly distinguish obstructive pulmonary disease (mainly pneumonia) from tumor and asthma patients, thereby gaining valuable time for the obstructive pulmonary disease patients.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1, the respiratory system disease intelligent auxiliary diagnosis decision-making method facing the basic medical institution is characterized in that: it comprises the following steps:
1) collecting an image; the scanning of the common film can be carried out, and the electronic file picture of the electronic CT can also be carried out;
2) converting the image into a digital image and importing the digital image into a computer;
3) carrying out image preprocessing, image enhancement and lesion area segmentation by adopting an algorithm;
4) extracting the characteristics of the lesion area by adopting an algorithm;
5) making a judgment according to the characteristic data obtained in the step 4, and if the judgment result is one of tumor and asthma, issuing the result to a decision maker; if the judgment result is the obstructive pulmonary disease, sending an oxygenation index detection request to a decision maker and synchronously starting mechanical assisted ventilation (oxygen inhalation or a breathing machine); as can be easily understood, the starting of mechanical assisted ventilation inevitably affects the oxygenation index, but as long as the decision maker detects the oxygenation index in a shorter time, the influence caused by ventilation is smaller, so that the final correct diagnosis cannot be substantially affected, and the starting of mechanical assisted ventilation in advance can reduce the substantial damage of a patient caused by the decrease of the oxygenation index;
6) and dynamically managing and controlling the mechanical auxiliary ventilation according to the oxygenation index.
In the step 1, the image is acquired by adopting a CT flat scanning mode, the advantage is that the image imaging is fast, the image can be obtained by scanning, and part of CT equipment has an electronic data transmission function and can directly transmit the image to a computer, so that the step 2 is omitted.
In step 3, the image preprocessing and image enhancement include: a) denoising the input image to obtain a denoised image; b) carrying out edge extraction on the denoised image to obtain an edge image; c) carrying out image enhancement processing on the edge image to obtain a denoised edge-enhanced image; d) processing the denoised image by using a brightness-controllable histogram equalization method to obtain a global enhanced image; e) linearly superposing the images obtained in the step c and the step d to obtain a final output image; the lesion region segmentation comprises f) detecting an initial contour of the object of interest from the image; g) classifying points on the initial contour according to the contrast characteristic; h) constructing different energy function models for different kinds of points, calculating an evolution function of a curve for image segmentation based on the constructed energy function models, and moving a level set curve in an image according to the evolution function; i) and determining whether the shifted level set curve has converged, outputting the shifted level set curve as a result of image segmentation if the shifted level set curve is determined to have converged, and performing the steps g) and h) again with the shifted level set curve as an initial contour if the shifted level set curve is determined not to have converged until a converged level set curve is obtained.
In step 4, the extracting of the features includes: firstly, reducing a target image obtained by segmentation into pictures with the size of 16 × 16, wherein the total number of the pictures is 256 pixels; secondly, converting the reduced picture into 256-level gray; thirdly, calculating the gray values of 256 pixel points; fourthly, calculating an average gray value; comparing the gray value of each pixel point with the average gray value, wherein the gray value is greater than or equal to the record 1 of the average value and is less than the record 0 of the average value; and sixthly, combining the comparison results of the step five together to form a 256-bit integer, wherein the integer is the feature code of the picture.
In step 5, the historical images of the three types of lung diseases are processed in steps 1-4 and then respectively recorded in feature databases of the three types of lung diseases, and then the images of the lung diseases to be judged are processed in steps 1-4 and then subjected to a k-nearest neighbor algorithm with feature codes in the feature databases to judge which classification the input data belongs to. Because the images of the three types of lung diseases are very different, no white spots exist in the images of asthma, the white spots of tumors are generally in a shape of a lump and have strong continuity, and have burr edges or smooth edges, and the obstructive pulmonary diseases (mainly pneumonia) are generally in a shape of frosty glass, the three types of lung diseases can be classified efficiently through the algorithm, and the work intensity of a doctor for seeing the lung is reduced.
In the step 5, the oxygenation index is detected by using a blood gas analyzer, and the oxygenation index is tested by using an electrode attaching mode, so that continuous detection and non-invasion can be realized.
Wherein, in step 6, the dynamic management and control includes: setting the oxygenation index range of oxygen supply, starting oxygen inhalation early warning when the real-time oxygenation index is in the range, starting a breathing machine early warning when the oxygenation index still does not rise after the oxygen inhalation is started for 20 seconds, starting a danger early warning when the oxygenation index still does not rise after the breathing machine is started for 30 seconds, and enabling medical personnel to take artificial membrane lung or other severe treatment means according to on-site medical conditions.
The foregoing embodiments and description have been presented only to illustrate the principles and preferred embodiments of the invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention as hereinafter claimed.
Claims (7)
1. The respiratory system disease intelligent auxiliary diagnosis decision-making method facing the primary medical institution is characterized in that: it comprises the following steps:
1) collecting an image;
2) converting the image into a digital image and importing the digital image into a computer;
3) carrying out image preprocessing, image enhancement and lesion area segmentation by adopting an algorithm;
4) extracting the characteristics of the lesion area by adopting an algorithm;
5) making a judgment according to the characteristic data obtained in the step 4, and if the judgment result is one of tumor and asthma, issuing the result to a decision maker; if the judgment result is the obstructive pulmonary disease, sending an oxygenation index detection request to a decision maker and synchronously starting mechanical assisted ventilation (oxygen inhalation or a breathing machine); as can be easily understood, the starting of mechanical assisted ventilation inevitably affects the oxygenation index, but as long as the decision maker detects the oxygenation index in a shorter time, the influence caused by ventilation is smaller, so that the final correct diagnosis cannot be substantially affected, and the starting of mechanical assisted ventilation in advance can reduce the substantial damage of a patient caused by the decrease of the oxygenation index;
6) and dynamically managing and controlling the mechanical auxiliary ventilation according to the oxygenation index.
2. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in the step 1, an image is acquired by adopting a CT flat scanning mode.
3. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in step 3, the image preprocessing and image enhancement comprise: a) denoising the input image to obtain a denoised image; b) carrying out edge extraction on the denoised image to obtain an edge image; c) carrying out image enhancement processing on the edge image to obtain a denoised edge-enhanced image; d) processing the denoised image by using a brightness-controllable histogram equalization method to obtain a global enhanced image; e) linearly superposing the images obtained in the step c and the step d to obtain a final output image; the lesion region segmentation comprises f) detecting an initial contour of the object of interest from the image; g) classifying points on the initial contour according to the contrast characteristic; h) constructing different energy function models for different kinds of points, calculating an evolution function of a curve for image segmentation based on the constructed energy function models, and moving a level set curve in an image according to the evolution function; i) and determining whether the shifted level set curve has converged, outputting the shifted level set curve as a result of image segmentation if the shifted level set curve is determined to have converged, and performing the steps g) and h) again with the shifted level set curve as an initial contour if the shifted level set curve is determined not to have converged until a converged level set curve is obtained.
4. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in step 4, the extraction of the features comprises: firstly, reducing a target image obtained by segmentation into pictures with the size of 16 × 16, wherein the total number of the pictures is 256 pixels; secondly, converting the reduced picture into 256-level gray; thirdly, calculating the gray values of 256 pixel points; fourthly, calculating an average gray value; comparing the gray value of each pixel point with the average gray value, wherein the gray value is greater than or equal to the record 1 of the average value and is less than the record 0 of the average value; and sixthly, combining the comparison results of the step five together to form a 256-bit integer, wherein the integer is the feature code of the picture.
5. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in step 5, the historical images of the three types of lung diseases are respectively recorded in the feature databases of the three types of lung diseases after being processed in steps 1-4, and then the images of the lung diseases needing to be judged are processed in steps 1-4 and then are subjected to a k-nearest neighbor algorithm with feature codes in the feature databases to judge which classification the input data belong to.
6. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in step 5, a blood gas analyzer is used for detecting the oxygenation index.
7. The intelligent aided diagnosis decision-making method for respiratory system diseases of the basic medical institution as claimed in claim 1, wherein: in step 6, the dynamic management and control includes: setting the oxygenation index range of oxygen supply, starting oxygen inhalation early warning when the real-time oxygenation index is in the range, starting a breathing machine early warning when the oxygenation index still does not rise after the oxygen inhalation is started for 20 seconds, and starting a disease risk early warning when the oxygenation index still does not rise after the breathing machine is started for 30 seconds.
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