1)Sim Y, Chung MJ, Kotter E, et al. Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs. Radiology 2020 ; 294 : 199-209.
2)Sung J, Park S, Lee SM, et al. Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs : A Randomized Crossover Study. Radiology 2021 ; 299 : 450-9.
3)Matsumoto T, Kodera S, Shinohara H, et al. Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning. Int Heart J 2020 ; advpub.
4)Matsuoka R, Akazawa H, Kodera S, et al. Deep learning-based approach for detecting signs of atrial septal defect on chest radiographs : a proof of concept study. medRxiv 2022 : 2022.2001.2030.22270137.
5)Tison GH, Sanchez JM, Ballinger B, et al. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch. JAMA Cardiol 2018 ; 3 : 409-16.
6)Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 2019 ; 381 : 1909-17.
7)Ford C, Xie CX, Low A, et al. Comparison of 2 Smart Watch Algorithms for Detection of Atrial Fibrillation and the Benefit of Clinician Interpretation : SMART WARS Study. JACC Clin Electrophysiol 2022 ; 8 : 782-91.
8)Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm : a retrospective analysis of outcome prediction. Lancet 2019 ; 394 : 861-7.
9)Katsushika S, Kodera S, Nakamoto M, et al. The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms. Int Heart J 2021 ; 62 : 1332-41.
10)Sawano S, Kodera S, Katsushika S, et al. Deep learning model to detect significant aortic regurgitation using electrocardiography. J Cardiol 2022 ; 79 : 334-41.