A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation
- Authors
- Kim, Cherry; Lee, Gaeun; Oh, Hongmin; Jeong, Gyujun; Kim, Sun Won; Chun, Eun Ju; Kim, Young-Hak; Lee, June-Goo; Yang, Dong Hyun
- Issue Date
- 3월-2022
- Publisher
- SPRINGER
- Keywords
- Heart valve diseases; Radiography; Cardiovascular system; Artificial intelligence; Deep learning
- Citation
- EUROPEAN RADIOLOGY, v.32, no.3, pp.1558 - 1569
- Indexed
- SCIE
SCOPUS
- Journal Title
- EUROPEAN RADIOLOGY
- Volume
- 32
- Number
- 3
- Start Page
- 1558
- End Page
- 1569
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/137616
- DOI
- 10.1007/s00330-021-08296-9
- ISSN
- 0938-7994
- Abstract
- Objectives Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). Methods We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. Results CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 +/- 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. Conclusions The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes.
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Collections - College of Medicine > Department of Medical Science > 1. Journal Articles
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