Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility

• Published in: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA sports traumatology, arthroscopy : official journal of the ESSKA, 2023-12, Vol.31 (12), p.5885-5895
• Main Authors: Stotter, Christoph, Klestil, Thomas, Chen, Kenneth, Hummer, Allan, Salzlechner, Christoph, Angele, Peter, Nehrer, Stefan
• Format: Article
• Language: English
• Subjects: Algorithms; Alignment; Ankle; Artificial Intelligence; Automation; Bayes Theorem; Bayesian analysis; Biomedical materials; Correlation coefficient; Correlation coefficients; Deep learning; Deformity analysis; HTO; Humans; Knee; Knee Joint - surgery; Leg; Machine learning; Medicine; Medicine & Public Health; Orthopedics; Osteoarthritis, Knee - surgery; Osteotomy; Osteotomy - methods; Radiographs; Radiography; Reliability analysis; Reproducibility; Reproducibility of Results; Retrospective Studies; Software; Sports Medicine; Tibia - diagnostic imaging; Tibia - surgery
• ISSN: 0942-2056; 1433-7347
• DOI: 10.1007/s00167-023-07644-0

Purpose The aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs). Methods Long-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip–knee–ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA). All measurements were performed twice and the performance of the AI software was compared with individual human readers using a Bayesian mixed model. In addition, the inter-observer intraclass correlation coefficient (ICC) for inter-observer reliability was evaluated by comparing measurements from manual readers. The intra-reader variability for manual measurements and the AI-based software was evaluated using the intra-observer ICC. Results Initial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36° ± 3.03° and 5.47° ± 2.90° to − 0.70 ± 2.34 and − 0.54 ± 2.31), MAD (19.38 mm ± 11.39 mm and 20.17 mm ± 10.99 mm to − 2.68 ± 8.75 and − 2.10 ± 8.61) and MPTA (86.29° ± 2.42° and 86.08° ± 2.34° to 91.6 ± 3.0 and 91.81 ± 2.54) changed significantly from pre- to postoperative, while JLCA and mLDFA were not altered. The fully automated AI-based analyses showed no significant differences for all measurements compared with manual reads neither in native preoperative radiographs nor postoperatively after HTO. Mean absolute differences between the AI-based software and mean manual observer measurements were 0.5° or less for all measurements. Inter-observer ICCs for manual measurements were good to excellent for all measurements, except for JLCA, which showed moderate inter-observer ICCs. Intra-observer ICCs for manual measurements were excellent for all measurements, except for JLCA and for MPTA postoperatively. For the AI-aided analyses, repeated measurements showed entirely consistent results for all measurements with an intra-observer ICC of 1.0. Conclusions The AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO wi