JP2021501633A5 - - Google Patents
Download PDFInfo
- Publication number
- JP2021501633A5 JP2021501633A5 JP2020524199A JP2020524199A JP2021501633A5 JP 2021501633 A5 JP2021501633 A5 JP 2021501633A5 JP 2020524199 A JP2020524199 A JP 2020524199A JP 2020524199 A JP2020524199 A JP 2020524199A JP 2021501633 A5 JP2021501633 A5 JP 2021501633A5
- Authority
- JP
- Japan
- Prior art keywords
- echocardiogram
- pair
- echocardiograms
- implementation method
- computer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Claims (12)
各対の連続する心エコー図のそれぞれに関連付けられる前記診断ステートメント間の自動比較によって、関連付けられるクラスを決定するために連続する心エコー図の各対を分析するステップと、
連続する心エコー図の各対について、畳み込み深層学習ネットワークの全結合層において、前記各対における前記心エコー図に1回以上の畳み込み及び/又はリダクションを行うことによって、特徴ベクトル又はデータのセットのいずれかを含む、各心エコー図の抽象的表現を決定するステップと、
前記複数対の連続する心エコー図の前記抽象的表現に基づいて、新しい1対の心エコー図のクラスを決定するために予測モデルをトレーニングするステップと、
を含み、
前記クラスは、前記各対における前記連続する心エコー図間に変化があるかないかを示し、前記変化は、心機能及び/又は心臓の構造の変化を表し、
前記抽象的表現は、前記各対の前記クラスを示す1つ以上の特徴を含む、心エコー図を分析するコンピュータ実施方法。 A step of obtaining a plurality of pairs of consecutive echocardiograms for a plurality of subjects from a database , wherein each echocardiogram contains the contents of the echocardiogram containing one or more diagnostic statements entered by the user. Steps to get, with instructions associated with,
A step of analyzing each pair of successive echocardiograms to determine the associated class by automatic comparison between the diagnostic statements associated with each of the successive echocardiograms of each pair.
For each pair of continuous echocardiograms, a set of feature vectors or data by performing one or more convolutions and / or reductions on the echocardiogram in each pair in the fully connected layer of the convolutional deep learning network. Steps to determine the abstract representation of each echocardiogram , including one,
A step of training a predictive model to determine a new pair of echocardiographic classes based on the abstract representation of the pair of consecutive echocardiograms.
It includes,
The class indicates whether there is a change between the successive echocardiograms in each pair, the change representing a change in cardiac function and / or structure of the heart.
The abstract representation is a computerized method of analyzing an echocardiogram that includes one or more features indicating the class of each pair.
前記コンピュータ実施方法は、各心エコー図が同数の画像フレームを含むように、前記各対の心エコー図の一方又は両方について1つ以上の画像フレームを補間するステップを更に含む、請求項1又は2に記載のコンピュータ実施方法。 Each echocardiogram contains multiple image frames
The computer-implemented method further comprises interpolating one or more image frames for one or both of the pairs of echocardiograms so that each echocardiogram contains the same number of image frames. 2. The computer implementation method according to 2.
前記被験者の過去の心エコー図を取得するステップと、
前記新しい心エコー図及び前記過去の心エコー図のクラスを決定するために前記予測モデルを使用するステップと、
を更に含む、請求項1から3のいずれか一項に記載のコンピュータ実施方法。 Steps to receive a new echocardiogram of the subject,
The step of acquiring the past echocardiogram of the subject and
With the steps of using the predictive model to determine the class of the new echocardiogram and the past echocardiogram.
The computer implementation method according to any one of claims 1 to 3, further comprising.
前記コンピュータ実施方法は、前記新しい1対の心エコー図の決定された前記クラスが、変化がないことを示す場合、ユーザに前記関連付けられる指示及び/又は前記内容を提供するようにインターフェースを制御するステップを更に含む、請求項4又は5に記載のコンピュータ実施方法。 The step of acquiring a past echocardiogram includes a step of receiving an instruction associated with the contents of the past echocardiogram.
The computer-implemented method controls the interface to provide the user with the associated instructions and / or the contents if the determined class of the new pair of echocardiograms indicates no change. The computer implementation method of claim 4 or 5 , further comprising a step.
各シーケンスは、前記被験者の異なるビューを表し、
前記連続する心エコー図の各対を分析するステップは、単一の画像を形成するために各心エコー図の前記複数のシーケンスを結合するステップを更に含む、請求項1から6のいずれか一項に記載のコンピュータ実施方法。 Each echocardiogram contains multiple sequences
Each sequence represents a different view of the subject.
Step of analyzing the respective pair of echocardiogram said consecutive, further comprising the step of coupling the plurality of sequences of Kakukokoro echocardiogram to form a single image, any one of claims 1 to 6 one The computer implementation method described in the section.
各シーケンスは、前記被験者の異なるビューを表し、
前記連続する心エコー図の各対を分析するステップは、前記各対の一方の心エコー図における複数のシーケンスのそれぞれを、前記各対の他方の心エコー図における複数のシーケンスのそれぞれと比較するステップを更に含む、請求項1から6のいずれか一項に記載のコンピュータ実施方法。 Each echocardiogram contains multiple sequences
Each sequence represents a different view of the subject.
The step of analyzing each pair of the successive echocardiograms compares each of the plurality of sequences in one echocardiogram of the pair with each of the plurality of sequences in the other echocardiogram of the pair. The computer implementation method according to any one of claims 1 to 6 , further comprising a step.
各シーケンスは、前記被験者の異なるビューを表し、
前記コンピュータ実施方法は、前記複数のシーケンスのそれぞれにビュータグを関連付けるステップを更に含み、
前記ビュータグは、前記シーケンスによって表される前記被験者のビューを示す、請求項1から8のいずれか一項に記載のコンピュータ実施方法。 Each echocardiogram contains multiple sequences
Each sequence represents a different view of the subject.
The computer implementation method further comprises associating a view tag with each of the plurality of sequences.
The computer implementation method according to any one of claims 1 to 8 , wherein the view tag indicates a view of the subject represented by the sequence.
各対の連続する心エコー図のそれぞれに関連付けられる前記診断ステートメント間の自動比較によって、関連付けられるクラスを決定するために連続する心エコー図の各対を分析し、
連続する心エコー図の各対について、畳み込み深層学習ネットワークの全結合層において、前記各対における前記心エコー図に1回以上の畳み込み及び/又はリダクションを行うことによって、特徴ベクトル又はデータのセットのいずれかを含む、各心エコー図の抽象的表現を決定し、
前記複数対の連続する心エコー図の前記抽象的表現に基づいて、新しい1対の心エコー図のクラスを決定するために予測モデルをトレーニングする、
プロセッサを含み、
前記クラスは、前記各対における前記連続する心エコー図間に変化があるかないかを示し、前記変化は、心機能及び/又は心臓の構造の変化を表し、
前記抽象的表現は、前記各対の前記クラスを示す1つ以上の特徴を含む、
心エコー図を分析する装置。 From the database, multiple pairs of consecutive echocardiograms of multiple subjects are obtained, and each echocardiogram contains instructions associated with the contents of the echocardiogram, including one or more diagnostic statements entered by the user. Have,
By automatic comparison between the diagnostic statements associated with each of the successive echocardiographic pairs of each pair, each pair of successive echocardiographic diagrams is analyzed to determine the associated class.
For each pair of continuous echocardiograms, a set of feature vectors or data by performing one or more convolutions and / or reductions on the echocardiogram in each pair in the fully connected layer of the convolutional deep learning network. Determine the abstract representation of each echocardiogram , including one
Based on the abstract representation of the pair of consecutive echocardiograms, a predictive model is trained to determine a new pair of echocardiographic classes.
It includes a processor,
The class indicates whether there is a change between the successive echocardiograms in each pair, the change representing a change in cardiac function and / or structure of the heart.
The abstract representation comprises one or more features indicating the class of each pair.
A device that analyzes echocardiograms.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201762580491P | 2017-11-02 | 2017-11-02 | |
US62/580,491 | 2017-11-02 | ||
PCT/EP2018/079963 WO2019086586A1 (en) | 2017-11-02 | 2018-11-02 | A method and apparatus for analysing echocardiograms |
Publications (3)
Publication Number | Publication Date |
---|---|
JP2021501633A JP2021501633A (en) | 2021-01-21 |
JP2021501633A5 true JP2021501633A5 (en) | 2021-12-09 |
JP7325411B2 JP7325411B2 (en) | 2023-08-14 |
Family
ID=64270837
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2020524199A Active JP7325411B2 (en) | 2017-11-02 | 2018-11-02 | Method and apparatus for analyzing echocardiogram |
Country Status (5)
Country | Link |
---|---|
US (1) | US20210219922A1 (en) |
EP (1) | EP3704707B1 (en) |
JP (1) | JP7325411B2 (en) |
CN (1) | CN111448614B (en) |
WO (1) | WO2019086586A1 (en) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US12001939B2 (en) * | 2018-12-11 | 2024-06-04 | Eko.Ai Pte. Ltd. | Artificial intelligence (AI)-based guidance for an ultrasound device to improve capture of echo image views |
US11446009B2 (en) * | 2018-12-11 | 2022-09-20 | Eko.Ai Pte. Ltd. | Clinical workflow to diagnose heart disease based on cardiac biomarker measurements and AI recognition of 2D and doppler modality echocardiogram images |
US11931207B2 (en) * | 2018-12-11 | 2024-03-19 | Eko.Ai Pte. Ltd. | Artificial intelligence (AI) recognition of echocardiogram images to enhance a mobile ultrasound device |
US11301996B2 (en) * | 2018-12-11 | 2022-04-12 | Eko.Ai Pte. Ltd. | Training neural networks of an automatic clinical workflow that recognizes and analyzes 2D and doppler modality echocardiogram images |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5911133A (en) * | 1997-10-22 | 1999-06-08 | Rush-Presbyterian -St. Luke's Medical Center | User interface for echocardiographic report generation |
US6447450B1 (en) * | 1999-11-02 | 2002-09-10 | Ge Medical Systems Global Technology Company, Llc | ECG gated ultrasonic image compounding |
CN1725978A (en) * | 2002-12-13 | 2006-01-25 | 皇家飞利浦电子股份有限公司 | System and method for processing a series of image frames representing a cardiac cycle |
JP2007526016A (en) | 2003-06-25 | 2007-09-13 | シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド | System and method for automatic local myocardial assessment of cardiac imaging |
CN103153197B (en) | 2010-12-13 | 2016-03-02 | 株式会社东芝 | Diagnostic ultrasound equipment, image processing apparatus and image processing method |
RU2699416C2 (en) * | 2014-09-10 | 2019-09-05 | Конинклейке Филипс Н.В. | Annotation identification to image description |
JP6467041B2 (en) | 2015-06-03 | 2019-02-06 | 株式会社日立製作所 | Ultrasonic diagnostic apparatus and image processing method |
JP6697743B2 (en) * | 2015-09-29 | 2020-05-27 | パナソニックIpマネジメント株式会社 | Information terminal control method and program |
EP3356971B1 (en) | 2015-10-02 | 2024-03-13 | Koninklijke Philips N.V. | System for mapping findings to pertinent echocardiogram loops |
CN106846306A (en) * | 2017-01-13 | 2017-06-13 | 重庆邮电大学 | A kind of ultrasonoscopy automatic describing method and system |
EP3625763A1 (en) * | 2017-05-18 | 2020-03-25 | Koninklijke Philips N.V. | Convolutional deep learning analysis of temporal cardiac images |
CN107184198A (en) * | 2017-06-01 | 2017-09-22 | 广州城市职业学院 | A kind of electrocardiosignal classifying identification method |
-
2018
- 2018-11-02 JP JP2020524199A patent/JP7325411B2/en active Active
- 2018-11-02 US US16/760,678 patent/US20210219922A1/en active Pending
- 2018-11-02 CN CN201880078066.9A patent/CN111448614B/en active Active
- 2018-11-02 EP EP18800551.6A patent/EP3704707B1/en active Active
- 2018-11-02 WO PCT/EP2018/079963 patent/WO2019086586A1/en unknown
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP2021501633A5 (en) | ||
US10930386B2 (en) | Automated normality scoring of echocardiograms | |
CN110516161B (en) | Recommendation method and device | |
CN107392255B (en) | Generation method and device of minority picture sample, computing equipment and storage medium | |
US11813113B2 (en) | Automated extraction of echocardiograph measurements from medical images | |
JP6182242B1 (en) | Machine learning method, computer and program related to data labeling model | |
US11410464B2 (en) | Detection of hand gestures using gesture language discrete values | |
US11468664B2 (en) | Machine learning to predict cognitive image composition | |
CN109902665A (en) | Similar face retrieval method, apparatus and storage medium | |
US20200019887A1 (en) | Data-driven activity prediction | |
JP2020502712A (en) | Disease diagnosis system and method using neural network | |
CN110046706B (en) | Model generation method and device and server | |
CN105426929B (en) | Object shapes alignment device, object handles devices and methods therefor | |
CN110009640A (en) | Handle method, equipment and the readable medium of heart video | |
CN111159419A (en) | Knowledge tracking data processing method, system and storage medium based on graph convolution | |
CN113065045A (en) | Method and device for carrying out crowd division and training multitask model on user | |
CN110110066A (en) | A kind of interaction data processing method, device and computer readable storage medium | |
CN109033078B (en) | The recognition methods of sentence classification and device, storage medium, processor | |
CN106709829A (en) | On-line-question-database-based learning condition diagnosis method and system | |
CN112035567B (en) | Data processing method, device and computer readable storage medium | |
CN108959594A (en) | A kind of ability level appraisal procedure and device based on time-variant weights | |
Hernández-Orallo et al. | Measuring cognitive abilities of machines, humans and non-human animals in a unified way: towards universal psychometrics | |
Muro et al. | Computing Murphy-Topel-corrected variances in a heckprobit model with endogeneity | |
Patel et al. | Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Function on Model Training Using MIMANSA | |
JP2023104067A (en) | Machine learning system and machine learning method |