Deep learning models for osteonecrosis of the femoral head on MRI: a systematic review and meta-analysis of diagnostic performance, staging accuracy, and external generalizability.

Diagnostic Accuracy of Medical Imaging–Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis

AI models demonstrate high diagnostic accuracy in imaging-based ONFH diagnosis, however, the current evidence is constrained by the limited number of included studies, predominantly retrospective designs, and a lack of adequate external validation, and should therefore be interpreted with caution.

Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis.

Diagnostic accuracy of deep learning models for detecting wrist and distal forearm fractures in pediatric patients: A systematic review and meta-analysis.

Deep learning-based segmentation of abdominal aortic aneurysm: a systematic review and meta-analysis of performance and clinical applicability

Abdominal aortic aneurysm (AAA) management relies heavily on imaging for surveillance, treatment planning, and follow-up. Deep learning (DL)-based segmentation may improve the efficiency and reproducibility of AAA image analysis; however, reported performance varies across studies. This study aimed to systematically re...

Deep learning models for pancreatic cancer detection on CT: a meta-analysis

Findings suggest the potential of DL as a decision-support tool, but they should be interpreted with caution, and prospective multicenter validation studies are urgently needed before clinical integration can be recommended.

Development and Validation of an Interpretable Deep Learning Model for Automated Kellgren-Lawrence Grading of Knee Osteoarthritis Using X-Ray Imaging.

Accurate and reproducible Kellgren-Lawrence grading of radiographic knee osteoarthritis remains challenging, particularly for intermediate grades and anatomically heterogeneous compartments. We present X-VIG, an interpretable deep learning framework integrating paired anteroposterior and lateral knee radiographs via vi...