Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence

Abstract

Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020. Automated landmark placement was conducted using the AI-algorithm LAMA™ and the measurements were used to determine femoral, tibial and total leg-length. Linear regression equations were subsequently derived for stature estimation. The estimated regression equations have a shallower slope and larger intercept in males and females (Femur-male: slope = 2.08, intercept = 77.49; Femur-female: slope = 1.9, intercept = 79.81) compared to the formulae previously derived by Trotter and Gleser 1952 (Femur-male: slope = 2.38, intercept = 61.41; Femur-female: slope = 2.47, intercept = 54.13) and Trotter and Gleser 1958 (Femur-male: slope = 2.32, intercept = 65.53). All long-bone measurements showed a high correlation (r ≥ 0.76) with stature. The linear equations we derived tended to overestimate stature in short persons and underestimate stature in tall persons. The differences in slopes and intercepts from those published by Trotter and Gleser (1952, 1958) may result from an ongoing secular increase in stature. Our study illustrates that AI-algorithms are a promising new tool enabling large-scale measurements.

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