Affiliation:
1. Department of Orthopedic Surgery Linkou Chang Gung Memorial Hospital Taoyuan City Taiwan
2. Engineering Product Development Singapore University of Technology and Design Tampines Singapore
3. Department of Biomedical Engineering Chang Gung University Taoyuan City Taiwan
4. Department of Medical Imaging and Intervention Linkou Chang Gung Memorial Hospital Taoyuan City Taiwan
5. Comprehensive Sports Medicine Center Taoyuan Chang Gung Memorial Hospital Taoyuan City Taiwan
6. Department of Orthopedic Surgery Keelung Chang Gung Memorial Hospital Keelung City Taiwan
7. Department of Electronics Engineering and Program in Nano‐Electronic Engineering and Design Chang Gung University Taoyuan City Taiwan
Abstract
AbstractPurposeMultifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries.MethodsImages and demographic information from 120 patients who underwent ACL reconstruction surgery were used for this study. An AI‐driven model was developed to measure the posterior lateral tibial slope using the YOLOv8 algorithm. The accuracy of the lateral tibial slope, medial tibial slope and tibial longitudinal axis measurements was assessed, and the results reached high levels of reliability. This study employed machine learning and AI techniques to provide objective, consistent and efficient measurements of tibial slopes on MR images.ResultsThree distinct models were developed to derive AI‐based measurements. The study results revealed a substantial correlation between the measurements obtained from the AI models and those obtained by the orthopaedic surgeon across three parameters: lateral tibial slope, medial tibial slope and tibial longitudinal axis. Specifically, the Pearson correlation coefficients were 0.673, 0.850 and 0.839, respectively. The Spearman rank correlation coefficients were 0.736, 0.861 and 0.738, respectively. Additionally, the interclass correlation coefficients were 0.63, 0.84 and 0.84, respectively.ConclusionThis study establishes that the deep learning‐based method for measuring posterior tibial slopes strongly correlates with the evaluations of expert orthopaedic surgeons. The time efficiency and consistency of this technique suggest its utility in clinical practice, promising to enhance workflow, risk assessment and the customization of patient treatment plans.Level of EvidenceLevel III, cross‐sectional diagnostic study.