Comprehensive Summary
This systematic review assesses the statistical performance and clinical utility of machine learning (ML) models in predicting revision, secondary knee injury, or reoperations after anterior cruciate ligament reconstruction (ACLR). The study systematically analyzed nine studies comprising 125,427 patients with an average follow-up of 5.82 years (range 0.08–12.3 years). The authors reviewed three major databases (PubMed, MEDLINE, EMBASE) to identify ML models that predict revision, secondary ACL injury, or reoperations in ACLR patients. The review found that five of the nine studies (55.6%) reported mean area under the curve (AUC) values, with the strongest models ranging from 0.77 to 0.997, suggesting fair to excellent discrimination ability. Four studies (44.4%) reported mean concordance values, with the best models scoring between 0.67 and 0.713, indicating moderate accuracy. Two studies reported Brier scores, ranging between 0.10 and 0.18, signifying reasonable predictive precision. However, four studies revealed significant calibration errors, particularly at two- and five-year follow-up points, suggesting model miscalibration over time. ML models outperformed traditional statistical models in some cases. For example, Artificial Neural Networks (ANN) achieved an AUC of 0.842 compared to 0.601 for logistic regression, demonstrating better predictive capabilities. Additionally, Random Forest models achieved an AUC of 0.997 for secondary meniscal injury prediction, highlighting ML’s potential in orthopedic surgery. However, lack of external validation and inconsistent performance across different ML models limit real-world applicability at this stage.
Outcomes and Implications
The findings of this review underscore the promise and limitations of ML models in orthopedic surgery and ACL reconstruction. The high AUC scores (up to 0.997) suggest strong predictive power, particularly in detecting secondary meniscal injuries and revision risk, making these models valuable for preoperative risk assessment and patient counseling. However, discrepancies in calibration and variable concordance scores (0.67–0.713) limit their clinical reliability. From a healthcare efficiency standpoint, the ability to accurately predict ACLR failure and secondary injuries could lead to personalized rehabilitation protocols, better surgical planning, and reduced healthcare costs. By identifying patients at higher risk for complications, clinicians can tailor postoperative care strategies, potentially reducing reoperation rates and improving long-term knee function. Despite these advantages, the lack of external validation limits real-world implementation. Only two of the nine studies (22.2%) performed external validation, meaning that many models remain untested in diverse patient populations. Additionally, significant miscalibration at the two- and five-year marks raises concerns about model reliability over time, necessitating further refinements and retraining using larger, more diverse datasets. To fully integrate ML into ACL reconstruction workflows, future research should focus on multimodal ML models, incorporating imaging data, surgical techniques, and patient demographics. Additionally, comparing ML models against traditional statistical methods in larger prospective studies will be essential for validating their clinical utility. If properly refined, ML-based prediction models could revolutionize ACL reconstruction by optimizing patient selection, surgical techniques, and long-term rehabilitation planning.