Comprehensive Summary
This systematic review explores the application of artificial intelligence (AI) in orthopedic imaging, focusing on its ability to enhance diagnostic accuracy, reduce processing time, improve image quality, create 3D models, and support treatment planning across various imaging modalities such as X-ray, MRI, CT, and ultrasound. The review screened 1,345 studies and analyzed 53 eligible articles, encompassing over 11 million images. It evaluated the performance of AI architectures like convolutional neural networks (CNNs), generative adversarial networks (GANs), and U-Net architectures in tasks such as segmentation, image enhancement, and diagnostic prediction. The AI models demonstrated strong performance, with segmentation tasks achieving Dice Similarity Coefficients (DSCs) up to 0.99, image enhancement models improving clarity and realism, and predictive models reaching high diagnostic accuracy. Despite these promising results, the review notes that full clinical integration of AI remains limited due to heterogeneity in study design, retrospective data, inconsistent outcome measures, and a lack of standardized protocols.
Outcomes and Implications
The research underscores AI's growing role in enhancing orthopedic imaging and supporting clinical decision-making. By analyzing over 11 million images, the study provides a comprehensive view of AI's current and potential clinical applications. The findings suggest that AI models, particularly CNNs, U-Nets, and GANs, are already achieving success in segmentation, measurement, and diagnostic prediction, making them well-suited for clinical integration. However, for widespread clinical use, more robust, prospective studies and validation are required, as current evidence is largely retrospective and lacks standardization. With these developments, AI could significantly improve orthopedic diagnostics and treatment planning, ultimately enhancing patient outcomes.