Algorithmic shortcutting in medical image analysis

Abstract

As deep learning (DL) gains prominence in medical image analysis, its applications in orthopedics and broader medical contexts have expanded significantly. While machine learning algorithms show potential in using X-ray images for predicting outcomes, biases may arise due to insufficient preprocessing. One such source of bias is algorithmic shortcutting, a phenomenon wherein DL models inadvertently train on patterns within training data unrelated to the intended diagnostic content. Leveraging the Osteoarthritis Initiative (OAI) dataset and ResNet18, our convolutional neural network (CNN) could predict the clinical center from which X-rays originated with over 95% accuracy. Moreover, our investigation unveiled significant variations in patient demographics and outcomes between clinical centers, implying that discerning the originating clinical center could be a potential shortcut in predicting patient outcomes from X-rays. These findings underscore the importance of careful X-ray image preparation and analysis. More broadly, as deep learning models increasingly influence clinical decision-making, this work underscores the importance of addressing model shortcutting to enhance the transparency, interpretability, and efficacy of deep learning research across orthopedics and other medical domains.