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.
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