AI-Assisted MRI Interpretation in Diagnosing Bankart and Reverse Bankart Lesions

Abstract

Background:

Accurate detection of glenoid labral tears remains challenging; however, it is essential for guiding treatment and return-to-play decisions in athletes. Magnetic resonance arthrography (MRA) improves diagnostic performance over noncontrast magnetic resonance imaging (MRI) but is invasive and more costly. Recent studies have shown that artificial intelligence (AI) models can accurately detect Bankart lesions on MRI and MRA, but whether AI assistance can improve clinician interpretation remains unknown.

Purpose:

To evaluate whether AI assistance improves clinician sensitivity and diagnostic confidence in detecting Bankart and reverse Bankart lesions on MRI and MRA.

Study Design:

Cohort study; Level of evidence, 3.

Methods:

A retrospective reader study was conducted using 586 shoulder examinations (335 MRI, 251 MRA) from 546 patients in the publicly available ScopeMRI data set, with arthroscopic findings as the reference standard. Custom AI models were trained to provide binary tear predictions for Bankart and reverse Bankart lesions. Four orthopaedic clinicians (2 fellowship-trained shoulder surgeons and 2 residents) independently reviewed all 117 test cases twice in a paired design: once unaided and once with AI assistance after a 60-day washout. The primary outcome was the change in sensitivity from unaided to aided interpretation, pooled across MRI and MRA, and evaluated separately for Bankart and reverse Bankart lesions. Secondary outcomes included changes in accuracy, specificity, and confidence.

Results:

For Bankart lesions, the mean sensitivity across clinicians increased from 38% unaided to 78.3% with AI assistance (P < .001), while accuracy increased from 77.1% to 84% (P = .002). For reverse Bankart lesions, sensitivity improved from 31.2% to 50% (P = .049). Specificity remained high across conditions. Across both pathologies, AI assistance corrected 51.1% of initially missed labral tears. Diagnostic confidence increased with AI assistance by 0.80 for noncontrast MRIs (P < .001) and 0.54 for MRAs (P < .001) on a 10-point scale.

Conclusion:

AI assistance significantly improved clinician sensitivity and diagnostic confidence for detecting Bankart and reverse Bankart lesions on MRI and MRA, with minimal impact on specificity.