• Pai-Hsueh Teng, Chia-Hao Liang, Yun Lin, Angel Alberich-Bayarri, Rafael López González, Pin-Wei Li, Yu-Hsin Weng, Yi-Ting Chen, Chih-Hsien Lin, Kang-Ju Chou, Yao-Shen Chen, and Fu-Zong Wu.
• Department of Radiology, Kaohsiung Veterans General Hospital.
• Medicine (Baltimore). 2021 Jun 11; 100 (23): e26270e26270.

AbstractThe aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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