Multicentric development and validation of a fully automated artificial intelligence system for planning medial open wedge high tibial osteotomy on weight‐bearing anterior‐posterior long leg radiographs: The ISAKOS John Joyce Award 2025

Purpose Preoperative planning, currently performed by orthopaedic surgeons (OS), is essential for successful osteotomy, but is labour‐intensive and error‐prone. As such, there is significant potential in the use of automated artificial intelligence (AI) algorithms to augment the abilities of OS in managing lower extremity pathologies in a high‐volume task that demands high precision and reliability. The aim was to develop a deep learning (DL) system capable of fully autonomous planning a medial open wedge high tibial osteotomy (mow‐HTO) on weight bearing anterior posterior (AP) long leg radiographs (LLR) and to validate its agreement with an expert‐derived reference standard, its reliability and processing time of the DL model compared to expert OS on multicentric test data sets (two institutions). Methods A total of 594 patients (age 41.1 ± 13.2 years, 182 female) who underwent osteotomy between 01/2014 and 01/2021 were retrospectively enrolled. Manual annotations for lower extremity alignment and high tibial landmarks were performed on AP LLR radiographs. The data set was divided into training (60%, n = 399), validation (10%, n = 59) and hold‐out testing (30%, n = 136), with an additional external dataset ( n = 100) for further testing. A DL system with twelve expert networks was trained to identify relevant landmarks for HTO planning. The DL model was then tested on fully automated planning of a medial open wedge HTO based on (1) the desired postoperative intersection of the mechanical axis with the tibial plateau (%‐based) and (2) the desired femorotibial axis (°‐based). Agreement with the expert‐derived reference, reliability and processing time were compared with three expert OS using FDA‐approved preoperative planning software on the test data sets. Results For preoperative alignment parameter analysis, compared to the ground truth, human preoperative accuracy ranged from 0.13° ± 0.10° to 1.06° ± 1.4°, interrater reliability ranged from intraclass correlation (ICC) 0.84–1.0 and clinically acceptable accuracy ranged from 72% to 100%. Preoperative accuracy of the DL model ranged from 0.22° ± 0.18° to 1.04° ± 1.28°, interrater reliability ranged from ICC 0.93–1.0 and clinically acceptable accuracy ranged from 91% to 100%. For the planning of the osteotomy gap, compared to the ground truth, human accuracy ranged from 0.46 ± 0.65 mm to 0.5 ± 0.88 mm (%‐based) and 0.43 ± 0.55 to 0.52 ± 0.75 mm (°‐based), reliability ranged from ICC 0.9–0.96 and 0.95–0.97 and clinically acceptable accuracy was 93.6%–96.0% (%‐based) and 89.8%–93.9% (°‐based). For the DL model, the accuracy was 0.53 ± 0.63 mm (%‐based) and 0.44 ± 0.53 mm (°‐based), reliability was ICC 0.99 (both methods) and clinically acceptable accuracy was 98.1% (%‐based) and 96.2% (°‐based). The DL‐model significantly and substantially outperformed the OS in intrarater reliability (0.88 vs. 1.0) and processing time for a fully automated alignment analysis (25.3 ± 0.8 s vs. 148.7 ± 10.1 s, p < 0.001). Conclusion The developed DL model enabled fully automated mow‐HTO planning on AP long‐leg radiographs with reliability comparable to that of expert OS while significantly reducing processing time and improving measurement consistency. These suggest that DL‐based systems may assist orthopaedic providers by accelerating and standardising osteotomy planning. Level of Evidence Level III, retrospective comparative study.