Comprehensive Summary
This literature review showcases an automated deep-learning-based image pipeline designed for large-scale knee radiograph registry creation. Researchers used supervised learning techniques and categorized 26,000 knee images by presence, laterality, prostheses, and radiographic views. The developed pipeline included an uncertainty-aware multilabel EfficientNet classifier, a domain detection classifier, and an object detection model capable of identifying 20 different knee implants. Results showcased exceptional performance, with F1 scores over 0.98 for classification, a 0.99 F1 score for domain detection, and mean average precision of 0.945 for object detection.
Outcomes and Implications
This literature review holds significant implications for improving the scalability and accuracy of knee radiograph analyses in medical registries, especially when creating large databases for medical research. By clearly identifying implants and other crucial details in knee images, doctors can better monitor how patients recover after surgery and improve their treatment plans. The practical nature of this AI-driven system means it could quickly become part of everyday healthcare routines, helping clinicians make more informed decisions and ultimately improve