Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

OBJECTIVES

Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements.

MATERIALS AND METHODS

The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated.

RESULTS

A total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45-5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and - 0.70-1.95 mm for lengths. On average, AI was 130 s faster than clinicians.

CONCLUSION

Automated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.