Diagnosis of SLAP lesions on shoulder MRI using a 2.5D deep learning and ensemble learning framework

The proposed automated diagnostic model, utilising a 2.5D deep learning and ensemble approach, demonstrated favourable diagnostic performance and clinical applicability for SLAP lesion detection on shoulder MRI. Among the ensemble strategies, the XGBoost model provided the highest sensitivity, rendering it particularly suitable as a clinical decision-support tool. The multi-slice information fusion framework substantially improved diagnostic accuracy, supporting its potential as a novel artificial intelligence solution to assist radiologists in diagnosing shoulder labral injuries.