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Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

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Author(s)
Mu Sook LeeYong Soo KimMinki KimMuhammad UsmanShi Sub ByonSung Hyun KimByoung Il LeeByoung-Dai Lee
Keimyung Author(s)
Lee, Mu Sook
Department
Dept. of Radiology (영상의학)
Journal Title
Sci Rep
Issued Date
2021
Volume
11
Abstract
We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.
Keimyung Author(s)(Kor)
이무숙
Publisher
School of Medicine (의과대학)
Citation
Mu Sook Lee et al. (2021). Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning. Sci Rep, 11, 16885. doi: 10.1038/s41598-021-96433-1
Type
Article
ISSN
2045-2322
Source
https://www.nature.com/articles/s41598-021-96433-1
DOI
10.1038/s41598-021-96433-1
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43985
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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