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Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds

ISSN: 2047-9980 ;EISSN: 2047-9980 ;DOI: 10.1161/JAHA.121.024168

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  • Title:
    Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds
  • Author: Cheng, Li Hsin ; Bosch, Pablo B.J ; Hofman, Rutger F.H ; Brakenhoff, Timo B ; Bruggemans, Eline F ; van der Geest, Rob J ; Holman, Eduard R
  • Subjects: 3-dimensional convolutional neural network ; aortic valve regurgitation ; apical 4-chamber cineloop ; echocardiography ; explainable artificial intelligence ; impaired left ventricular function
  • Description: BACKGROUND: With the increase of highly portable, wireless, and low-cost ultrasound devices and automatic ultrasound ac-quisition techniques, an automated interpretation method requiring only a limited set of views as input could make preliminary cardiovascular disease diagnoses more accessible. In this study, we developed a deep learning method for automated detection of impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical 4-chamber ultrasound cineloops and investigated which anatomical structures or temporal frames provided the most relevant information for the deep learning model to enable disease classification. METHODS AND RESULTS: Apical 4-chamber ultrasounds were extracted from 3554 echocardiograms of patients with impaired LV function (n=928), AV regurgitation (n=738), or no significant abnormalities (n=1888). Two convolutional neural networks were trained separately to classify the respective disease cases against normal cases. The overall classification accuracy of the impaired LV function detection model was 86%, and that of the AV regurgitation detection model was 83%. Feature importance analyses demonstrated that the LV myocardium and mitral valve were important for detecting impaired LV function, whereas the tip of the mitral valve anterior leaflet, during opening, was considered important for detecting AV regurgitation. CONCLUSIONS: The proposed method demonstrated the feasibility of a 3-dimensional convolutional neural network approach in detection of impaired LV function and AV regurgitation using apical 4-chamber ultrasound cineloops. The current study shows that deep learning methods can exploit large training data to detect diseases in a different way than conventionally agreed on methods, and potentially reveal unforeseen diagnostic image features.
  • Creation Date: 2022-08
  • Language: English
  • Identifier: ISSN: 2047-9980
    EISSN: 2047-9980
    DOI: 10.1161/JAHA.121.024168
  • Source: Vrije Universiteit
    GFMER Free Medical Journals
    PubMed Central
    Wiley Blackwell Open Access Titles
    ROAD: Directory of Open Access Scholarly Resources
    DOAJ Directory of Open Access Journals
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