Deep Learning Algorithms for Interpretation of Upper Extremity Radiographs: Laterality and Technologist Initial Labels as Confounding Factors.

Abstract

Convolutional neural networks (CNNs) trained to identify abnormalities on upper extremity radiographs achieved an AUC of 0.844 with a frequent emphasis on radiograph laterality and/or technologist labels for decision-making. Covering the labels increased the AUC to 0.857 (<i>p</i> = .02) and redirected CNN attention from the labels to the bones. Using images of radiograph labels alone, the AUC was 0.638, indicating that radiograph labels are associated with abnormal examinations. Potential radiographic confounding features should be considered when curating data for radiology CNN development.