Open AccessPerformance and educational training of radiographers in lung nodule or mass detection
Author(s) -
Pai Hsueh Teng,
Chia Hao Liang,
Yun Lin,
Ángel Alberich-Bayarri,
Rafael López González,
Pin Wei Li,
Yu Hsin Weng,
Yi Ting Chen,
Chih Hsien Lin,
Kang Ju Chou,
Yao Shen Chen,
Fu-Zong Wu
Publication year - 2021
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000026270
Subject(s) - medicine , chest radiograph , radiography , radiology , deep learning , lung , nodule (geology) , artificial intelligence , computer science , paleontology , biology
The 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 (AUC Mass : 0.916 vs AUC Trained 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.