A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

Cherry Kim, Gaeun Lee, Hongmin Oh, Gyujun Jeong, Sun Won Kim, Eun Ju Chun, Young‑Hak Kim, June‑Goo Lee, Dong Hyun Yang
European Radiology
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This study presents the development and validation of a deep learning-based automatic cardiovascular border (CB) analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD) using posterior-anterior chest radiographs (CXR). The CB_auto, developed using 1614 normal and VHD CXRs, demonstrated excellent reliability with an intraclass correlation coefficient above 0.98. When compared to manual CB drawing (CB_hand), the absolute percentage measurement error was under 10% for all parameters except for carinal angle and left atrial appendage. In addition, CB parameters were found to be significantly greater in VHD than in normal controls, and correlated significantly with echocardiographic measurements. This suggests that CB_auto could serve as a reliable tool for daily clinical practice and research purposes, enhancing diagnosis and evaluation of VHD. The proposed method is set to feature in a forthcoming product from Coreline Soft.


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