Wolfgart Julius Michael, Hofmann Ulf Krister, Praster Maximilian, Danalache Marina, Migliorini Filipo, Feierabend Martina
Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Hospital, 52074 Aachen, Germany; Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074 Aachen, Germany.
Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074 Aachen, Germany.
Knee. 2025 Jun;54:301-315. doi: 10.1016/j.knee.2025.02.032. Epub 2025 Mar 18.
Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy.
The present systematic review updates and discusses the existing literature regarding machine learning algorithm-based tools to predict outcome and management in patients after ACL reconstruction.
PubMed was searched for articles containing machine learning algorithms related to anterior cruciate ligament reconstruction and its outcome and management. No additional filters or time constraints were used. All eligible studies were accessed by hand.
After screening of 115 articles, 15 were included. Six studies evaluated predictors for reoperation after ACL surgery. Four studies investigated the clinical outcome prediction after ACL reconstruction including prediction of secondary meniscus tear and secondary knee osteoarthritis. Single topics addressed in patients with ACL reconstruction were costs, opioid use, the need for a femoral nerve block, short term complications, hospital admission, and reduction of the burden to complete the Knee Osteoarthritis and Outcome score questionnaire. Predictive power was very heterogeneous, depending on the specific research question and parameters included.
New machine-learning tools offer interesting insights into variables affecting the target outcome and general management of patients with ACL reconstruction. While present machine-learning based prediction models seem to outperform previous existing benchmark regression models, their predictive ability often is still too low to base individual decision making on them. With the rapid progress observed over the last few years, it is conceivable that this might change, however, in the foreseeable future.
基于机器学习的工具在临床实践中越来越受欢迎。它们提供了新的可能性，但在可靠性和准确性方面也存在局限性。
本系统评价更新并讨论了关于基于机器学习算法的工具预测前交叉韧带重建术后患者结局和管理的现有文献。
在PubMed上搜索包含与前交叉韧带重建及其结局和管理相关的机器学习算法的文章。未使用其他筛选条件或时间限制。所有符合条件的研究均通过人工检索获取。
在筛选的115篇文章中，纳入了15篇。六项研究评估了前交叉韧带手术后再次手术的预测因素。四项研究调查了前交叉韧带重建术后的临床结局预测，包括继发半月板撕裂和继发膝关节骨关节炎的预测。前交叉韧带重建患者涉及的单一主题包括费用、阿片类药物使用、股神经阻滞的必要性、短期并发症、住院、以及减轻完成膝关节骨关节炎和结局评分问卷的负担。预测能力差异很大，取决于具体的研究问题和纳入的参数。
新的机器学习工具为影响前交叉韧带重建患者目标结局和总体管理的变量提供了有趣的见解。虽然目前基于机器学习的预测模型似乎优于先前现有的基准回归模型，但其预测能力往往仍然过低，无法基于它们进行个体决策。然而，鉴于过去几年观察到的快速进展，可以想象在可预见的未来这种情况可能会改变。
