Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm

National Health Service, 2019 Moncada, 2011, Reading and interpretation of chest X-ray in adults with community-acquired pneumonia, Braz J Infect Dis, 15, 540, 10.1016/S1413-8670(11)70248-3 Hopstaken, 2004, Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections, Clin Radiol, 59, 743, 10.1016/j.crad.2004.01.011 Moifo, 2015, Inter-observer variability in the detection and interpretation of chest X-ray anomalies in adults in an endemic tuberculosis area, Open J Med Imag, 5, 143, 10.4236/ojmi.2015.53018 Sakurada, 2012, Inter-rater agreement in the assessment of abnormal chest X-ray findings for tuberculosis between two Asian countries, BMC Infect Dis, 12, 31, 10.1186/1471-2334-12-31 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216 Ting, 2017, Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes, JAMA, 318, 2211, 10.1001/jama.2017.18152 Bejnordi, 2017, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, JAMA, 318, 2199, 10.1001/jama.2017.14585 Hwang, 2019, Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs, JAMA Netw Open, 2, 10.1001/jamanetworkopen.2019.1095 Yoo, 2020, Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs, JAMA Netw Open, 3, 10.1001/jamanetworkopen.2020.17135 McHugh, 2012, Interrater reliability: the kappa statistic, Biochem Med (Zagreb), 22, 276, 10.11613/BM.2012.031 Huang, 2017, Densely connected convolutional networks Tan, 2019, EfficientNet: rethinking model scaling for convolutional neural networks Wang, 2017, ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases Paszke, 1912 Sim, 2020, Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs, Radiology, 294, 199, 10.1148/radiol.2019182465