Deep learning-based anomaly detection in orthopedic medical imaging: A systematic literature review

Abstract

In recent years, deep learning (DL) has revolutionized medical imaging, offering notable promise in orthopedic diagnostics. This systematic review explores how DL, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), are applied to detect and analyze orthopedic anomalies such as fractures, spinal deformities, and foot deformities. We reviewed 63 peer-reviewed studies published between 2017 and 2025, analyzing their methodologies, datasets, performance metrics, and clinical relevance. The findings reveal significant advancements in fracture classification and vertebral labeling, though challenges persist for subtle anomalies and less-represented deformities. Despite encouraging results, limitations include small sample sizes, lack of external validation especially for rare pathologies. We conclude by identifying research gaps and proposing future directions for developing robust, clinically integrated DL tools to enhance diagnostic accuracy, data diversity, and anomaly detection in complex orthopedic scenarios.

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