Cross-Center Validation of Deep Learning Model for Musculoskeletal Fracture Detection in Radiographic Imaging: A Feasibility Study

Abstract

Abstract Fractures, often resulting from trauma, overuse, or osteoporosis, pose diagnostic challenges due to their variable clinical manifestations. To address this, we propose a deep learning-based decision support system to enhance the efficacy of fracture detection in radiographic imaging. For the purpose of our study, we utilized 720 annotated musculoskeletal (MSK) X-rays from the MURA dataset, augmented by bounding box-level annotation, for training the YOLO (You Only Look Once) model. The model’s performance was subsequently tested on two datasets, sampled FracAtlas dataset (Dataset 1, 840 images, n NORMAL = 696, n FRACTURE = 144) and own internal dataset (Dataset 2, 124 images, n NORMAL = 50, n FRACTURE = 74), encompassing a diverse range of MSK radiographs. The results showed a Sensitivity ( Se ) of 0.910 (95% CI: 0.852–0.946) and Specificity ( Sp ) of 0.557 (95% CI: 0.520–0.594) on the Dataset 1, and a Se of 0.622 (95% CI: 0.508–0.724) and Sp of 0.740 (95% CI: 0.604–0.841) on the Dataset 2. This study underscores the promising role of AI in medical imaging, providing a solid foundation for future research and advancements in the field of radiographic diagnostics.

Ähnliche Arbeiten