Stotter, Christoph and Klestil, Thomas and Chen, Kenneth and Hummer, Allan and Salzlechner, Christoph and Angele, Peter and Nehrer, Stefan (2023) Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility. SPRINGER, NEW YORK.
Full text not available from this repository. (Request a copy)Abstract
PurposeThe 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).MethodsLong-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 (TM), 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.ResultsInitial varus malalignment was corrected to slight valgus alignment after HTO. Measured by the AI algorithm and manually HKA (5.36 degrees +/- 3.03 degrees and 5.47 degrees +/- 2.90 degrees 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 degrees +/- 2.42 degrees and 86.08 degrees +/- 2.34 degrees 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 degrees 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.ConclusionsThe AI-based software can provide fully automated analyses of native long-leg radiographs in patients with varus malalignment and after HTO with great accuracy and reproducibility and could support clinical workflows.Level of evidenceDiagnostic study, Level III.
| Item Type: | Other |
|---|---|
| Uncontrolled Keywords: | AUTOLOGOUS CHONDROCYTE IMPLANTATION; RELIABILITY; DEFORMITY; Deep learning; AI; Deformity analysis; HTO |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Unfallchirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 23 Apr 2024 09:21 |
| Last Modified: | 23 Apr 2024 09:21 |
| URI: | https://pred.uni-regensburg.de/id/eprint/59917 |
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