Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany.
Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany.
Knee Surg Sports Traumatol Arthrosc. 2022 Feb;30(2):376-388. doi: 10.1007/s00167-021-06848-6. Epub 2022 Jan 10.
Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.
A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation.
The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points.
The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters.
III.
人工智能（AI）在医疗保健领域的应用正在迅速发展，为数据分析提供了新的选择。机器学习（ML）是 AI 的一个独特应用，它能够进行预测，并已在不同的医学专业中使用各种方法进行了测试，例如诊断应用、成本预测或识别风险因素。在骨科领域，这项技术最近才被引入，关于膝关节置换中 ML 的文献也很少。在本综述中，我们旨在研究使用 ML 模型在膝关节置换中已经可以进行哪些预测，以确定有效使用这种新方法的前提条件。为此，我们对膝关节置换中用于结果预测的 ML 算法进行了系统综述。
对 PubMed、Medline 数据库和 Cochrane 图书馆进行了全面检索，以查找用于膝关节置换的 ML 应用。所有相关文章均由一名骨科医生和一名数据科学家根据 PRISMA 声明进行系统检索和评估。搜索策略共产生了 225 篇文章，最终有 19 篇被评估为符合条件。应用改良的 Coleman 方法学评分（mCMS）进行方法学评估。
本综述中呈现的研究结果表明（AUC 中位数 0.76/范围 0.57-0.98），结果具有良好的一致性，而分析的预测模型具有异质性：并发症（6）、成本（4）、功能结果（3）、翻修（2）、术后满意度（2）、手术技术（1）和生物力学特性（1）。mCMS 中位数为 65 分（范围 40-80 分）。
使用 ML 模型对特定数据进行特定结果的预测已经可行，然而，对更复杂结果的预测仍然不够准确。膝关节置换的登记数据尚未得到充分分析，因此特定参数尚未得到充分评估。纳入特定输入数据以及骨科医生和数据科学家的合作是充分利用膝关节置换中 ML 能力的必要前提。未来的研究应调查具有特定和纵向记录参数的前瞻性数据。
III。
