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Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
32 Pages Posted: 28 Nov 2018
More...Abstract
Background: Interpretation of chest radiographs (CRs) is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify CRs may help streamline the clinical workflow.
Methods: We developed a deep-learning-based automatic detection (DLAD) algorithm for classification of CRs with major thoracic diseases including pulmonary malignancy, active pulmonary tuberculosis, pneumonia, and pneumothorax, using 54,221 normal CRs and 47,917 abnormal CRs, which were labeled and annotated by board certified radiologists. Performance of DLAD was validated using five independent datasets from different institutions. To compare the performances of DLAD with physicians, an observer performance test was conducted by 15 physicians including non-radiology physicians, board-certified radiologists, and thoracic radiologists. Image-wise classification performances and lesion-wise localization performances were measured by area under the receiver operating characteristic (ROC) curves, and area under the alternative free-response ROC curves, respectively.
Findings: DLAD demonstrated classification performance of 0*973-1*000 and localization performance of 0*923-1*000 in independent datasets. DLAD demonstrated significantly higher performance than all three physician groups in both image-wise classification (0*983 vs. 0*814-0*932) and lesion-wise localization (0*985 vs. 0*781-0*907). Significant improvements in both image-wise classification (performance increment, 0*026-0*090) and lesion-wise localization (performance increment, 0*031-0*092) were observed in all three physician groups with assistance of the algorithm.
Interpretation: Our DLAD demonstrated excellent and consistent performance in the detection of major thoracic diseases on CR, outperforming even thoracic radiologists.
Funding Statement: The present study was supported by the Seoul National University Hospital Research fund (grant number: 04- 2016-3000), Lunit Inc. and the Seoul Research & Buisiness Development Program (grant number: FI170002).
Declaration of Interests: Chang Min Park reports grants from Seoul National University Hospital, Seoul Metropolitan Government, and Lunit Inc., during the conduct of the study; Sunggyun Park and Jaehong Aum are employees of Lunit Inc.
Ethics Approval Statement: The present study was approved by the institutional review boards of all participating institutions. Patients’ informed consents were waived.
Keywords: Deep Learning; Chest Radiograph; Lung Cancer; Tuberculosis; Pneumonia; Pneumothorax
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