• Arch Orthop Trauma Surg · Nov 2024

    A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.

    • Thomas J York, Bartosz Szyszka, Angela Brivio, Omar Musbahi, David Barrett, Justin P Cobb, and Gareth G Jones.
    • MSk Lab, Imperial College London, Sir Michael Uren Hub, 86 Wood Lane, London, W12 0BZ, UK.
    • Arch Orthop Trauma Surg. 2024 Nov 1; 144 (11): 496349684963-4968.

    IntroductionKnee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.Materials And MethodsThis research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy.Results1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).ConclusionsThe AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.© 2024. The Author(s).

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