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Abstract

Purpose:
To assess the diagnostic performances of artificial intelligence (AI)-based software to perform automatic measurements on full-leg radiographs.

Methods:
We retrospectively collected 393 consecutive full-leg radiographs including 50 EOS radiographs and 343 conventional radiographs from 4 imaging institutions. Two senior musculoskeletal radiologists independently annotated key points to measure the Hip-Knee-Ankle (HKA) angle, femur, tibial, and full-leg lengths, and pelvic obliquity. The gold standard was defined as the mean of their two measurements.
Statistical analysis consisted of mean absolute error (MAE), bias assessed with Bland-Altman analysis between the gold standard and the AI prediction and intraclass coefficient (ICC) between the two manual ratings.

Results:
153 full-leg conventional radiographs and 23 full-leg EOS radiographs were included, 217 radiographs were excluded. MAE for the HKA angle, femur, tibial, and full-leg lengths and pelvic obliquity were respectively 0.33° (95% CI: [0.29 ; 0.37], bias=0.28mm, ICC=0.99), 1.5mm (95% CI: [1.2 ; 1.9], bias=1.31mm, ICC=0.99), 1.5mm (95% CI: [1.2 ; 1.8], bias=0.33mm, ICC=0.99), 1.7mm (95% CI: [1.3 ; 2.1], bias=1.48mm, ICC=0.99),
0.7mm (95% CI: [0.6 ; 0.9], bias=-0.01mm, ICC=0.99). Bias and MAE between the gold standard and the AI prediction were low across all measurements. ICC between the two manual ratings was excellent across all measurements.

Conclusion:
AI allows accurate and automatic anatomic measurements on full-leg conventional and EOS radiographs.

Limitations:
The study is retrospective with a small number of radiographs and no comparison to an independent manual rating.