A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Weijie Fan, Yi Yang, Jing Qi, Qichuan Zhang, Cuiwei Liao, Li Wen, Shuang Wang, Guangxian Wang, Yu Xia, Qihua Wu, Xiaotao Fan, Xingcai Chen, Mi He, JingJing Xiao, Liu Yang, Yun Liu, Jia Chen, Bing Wang, Lei Zhang, Liuqing Yang, Hui Gan, Shushu Zhang, Guofang Liu, Xiaodong Ge, Yuanqing Cai, Gang Zhao, Xi Zhang, Mingxun Xie, Huilin Xu, Yi Zhang, Jiao Chen, Jun Li, Shuang Han, Ke Mu, Shilin Xiao, Tingwei Xiong, Yongjian Nian () and Dong Zhang ()
Additional contact information
Weijie Fan: Army Medical University
Yi Yang: Army Medical University
Jing Qi: Army Medical University
Qichuan Zhang: Army Medical University
Cuiwei Liao: Army Medical University
Li Wen: Army Medical University
Shuang Wang: Army Medical University
Guangxian Wang: Chongqing Medical University
Yu Xia: Xishui hospital of Traditional Chinese Medicine
Qihua Wu: People’s Hospital of Nanchuan
Xiaotao Fan: Fengdu People’s Hospital
Xingcai Chen: Army Medical University
Mi He: Army Medical University
JingJing Xiao: Army Medical University
Liu Yang: Army Medical University
Yun Liu: Army Medical University
Jia Chen: Army Medical University
Bing Wang: Army Medical University
Lei Zhang: Army Medical University
Liuqing Yang: Army Medical University
Hui Gan: Army Medical University
Shushu Zhang: Army Medical University
Guofang Liu: Army Medical University
Xiaodong Ge: Army Medical University
Yuanqing Cai: Army Medical University
Gang Zhao: Army Medical University
Xi Zhang: Army Medical University
Mingxun Xie: Army Medical University
Huilin Xu: Army Medical University
Yi Zhang: Army Medical University
Jiao Chen: Army Medical University
Jun Li: Army Medical University
Shuang Han: Army Medical University
Ke Mu: Army Medical University
Shilin Xiao: Army Medical University
Tingwei Xiong: Army Medical University
Yongjian Nian: Army Medical University
Dong Zhang: Army Medical University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

Date: 2024
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DOI: 10.1038/s41467-024-45599-z

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