S. Simon (Vienna, AT)
Orthopädisches Spital Speising GmbH Orthopädische AbteilungPresenter Of 1 Presentation
12.3.1 - Fully Automated Deep Learning for Knee Alignment Assessment in Lower Extremity Radiographs: A Cross-Sectional Diagnostic Study
Abstract
Purpose
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-model, by comparing its outputs with manually performed measurements.
Methods and Materials
The AI-model 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 January 2015 and August 2020 from male and female patients over 18 years. AI-software and two orthopedists measured all LLRs. Kellgren-Lawrence score was assessed and reading time of each examination was automatically recorded. All measurements were compared and interchangeability, mean absolute deviation (sMAD) and intraclass-correlation (ICC) were calculated.
Results
A total of 295 LLRs from 284 patients (mean age, 65years (18; 90); 97 (34.2%) men) were analyzed. The AI-model produces outputs on 98.0% of the LLRs and had an accuracy of 89.2% when comparing the AI-outputs to the manually measured Ground-truth (100%). AI vs. mean observer revealed an sMAD between 0.39-2.19° for angles and 1.45-5.00mm for lengths. AI showed good reliability in all lengths and angles (ICC≥0.87) compared to mean observers. Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) of 0.54° for the hip-knee-ankle-angle, 0.70mm for uncalibrated leg-length, and 6.70mm for calibrated leg-length. On average, AI was 130 seconds faster than clinicians.
Conclusion
Automated measurements of knee-alignment and length measurements produced with an AI tool result in reproducible, accurate measures with time savings compared to manually acquired measurements.