AI-accelerated prediction of optimal implant alignment in total knee arthroplasty
Linda ten Klooster (EEMCS-MIA), Periklis Tzanetis (ET-BDDP), Jelmer M. Wolterink (EEMCS-MIA), Nico Verdonschot (ET-BDDP)
Abstract
Background: Total knee arthroplasty (TKA) is effective for treating end-stage knee osteoarthritis (OA). However, dissatisfaction for one in five patients persists. A novel musculoskeletal model-based approach has been previously developed to predict the optimal implant position toward recreating the knee's pre-diseased functional behaviour, ultimately improving patient satisfaction post-operatively 1. However, its computational complexity poses challenges for its implementation in clinical practice. The study aims to accelerate optimal implant alignment estimation using an AI-based surrogate model for real-time predictions.
Methods: A previously obtained dataset of 21 knee OA patients2 was employed to train a neural network. It consists of candidate implant positions and their deviations from the knee’s pre-diseased state. The neural network was trained to learn this relation for individual patients, acting as a surrogate model enabling rapid determination of the optimal position with minimal error.
Results: For all patients candidate implant positions were examined to assess the network’s ability to predict the optimal position. Strong correlation between predictions and computed values in the musculoskeletal model was shown in 16 patients, while for 5 other patients this relation was weaker. Evaluation metrics include Spearman’s rank coefficient, Kendall Tau’s rank coefficient, and mean squared error. Factors that affect model performance include limited data and complexity in distinguishing subtle implant position variations. The computation time reduced drastically from approximately 32 hours to a maximum of 7 seconds for all patients.
Conclusion: The neural network significantly accelerates the prediction of optimal implant positioning in TKA, potentially aiding surgeons to predict optimal implant alignment in a personalized matter. Ultimately this improves patient satisfaction.
References
1. Tzanetis P, Fluit R, Souza K de, Robertson S, Koopman B, Verdonschot N. 2023. Pre-planning the surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty. Bioengineering 10: 543.
2. Tzanetis P, Souza K De, Robertson S, Fluit R, Koopman B, Verdonschot N. 2024. Numerical study of osteophyte effects on preoperative knee functionality in patients undergoing total knee arthroplasty. J Orthop Res : 1–12.