Predicting patient satisfaction after anterior cruciate ligament reconstruction based on morphology: a machine learning approach

Anterior cruciate ligament reconstruction (ACL-R) has a high success rate, but a subset of patients report unsatisfactory outcomes, highlighting the need for improved predictive tools. Recent studies have shown that bony morphology plays an important role in both the risk of ACL injury and post-reconstruction outcomes. This study aims to develop a framework for understanding the relationship between morphology and patient satisfaction. We explore the use of machine learning (ML) models to predict patient satisfaction following ACL reconstruction based on preoperative bone morphology. Using 3D models from high-resolution Magnetic Resonance (MR) imaging, 21 morphological parameters of the femur and tibia were analysed, including linear, surface and volumetric measurements. These were correlated with patient satisfaction as assessed by the Knee injury and Osteoarthritis Outcome Score Global (KOOS Global) questionnaire, administered six months after surgery. To streamline the analysis, automated systems based on statistical shape models and deep learning were employed for landmark placement and segmentation. A dataset of 45 patients was used to train and evaluate three ML models: Random Forest, Logistic Regression and Gradient Boosting. Dimensionality reduction identified 10 key morphological features and Gradient Boosting outperformed the other models, achieving accuracy of 0.89, precision of 0.90 and recall of 0.89. The results demonstrate the potential of combining advanced imaging, automated measurement and ML for personalized prediction of patient satisfaction. The predictive model shows that primarily femoral features, along with intercondylar notch volume and tibial measurements, play a critical role in determining patient satisfaction. This approach may guide surgical planning, patient counselling, intervention strategies and rehabilitation. Although promising, further research with larger datasets, additional parameters and advanced ML techniques is needed to improve model robustness and clinical applicability.