Academic Journal
Neural network surrogates for musculoskeletal models: An application to implant alignment in total knee arthroplasty.
| Τίτλος: | Neural network surrogates for musculoskeletal models: An application to implant alignment in total knee arthroplasty. |
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| Συγγραφείς: | Ten Klooster L; Radboud University Medical Center, Orthopaedic Research Laboratory, 6500 HB Nijmegen, the Netherlands; University of Twente, Department of Applied Mathematics, Technical Medical Centre, 7522 NB Enschede, the Netherlands. Electronic address: linda.tenklooster@radboudumc.nl., Tzanetis P; University of Twente, Department of Biomechanical Engineering, 7522 LW Enschede, the Netherlands., Janssen D; Radboud University Medical Center, Orthopaedic Research Laboratory, 6500 HB Nijmegen, the Netherlands., Bitter T; Radboud University Medical Center, Orthopaedic Research Laboratory, 6500 HB Nijmegen, the Netherlands., Wolterink JM; University of Twente, Department of Applied Mathematics, Technical Medical Centre, 7522 NB Enschede, the Netherlands. |
| Πηγή: | Journal of biomechanics [J Biomech] 2026 Mar; Vol. 198, pp. 113214. Date of Electronic Publication: 2026 Feb 14. |
| Τύπος έκδοσης: | Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: United States NLM ID: 0157375 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2380 (Electronic) Linking ISSN: 00219290 NLM ISO Abbreviation: J Biomech Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York ; Oxford : Elsevier Science |
| Ιατρικοί όροι (MeSH): | Arthroplasty, Replacement, Knee*/methods , Models, Biological* , Musculoskeletal System* , Multilayer Perceptrons* , Knee Prosthesis*, Ligaments/physiology ; Ligaments/physiopathology ; Humans ; Biomechanical Phenomena |
| Περίληψη: | Total knee arthroplasty (TKA) is an effective treatment for end-stage knee osteoarthritis (OA). However, dissatisfaction persists in one in five patients. Musculoskeletal model-based approaches have the potential to predict implant alignment that restores pre-diseased knee functioning and improves patient satisfaction, but their computational complexity limits clinical application. This study aimed to accelerate the estimation of optimal implant alignment using a neural network-based surrogate model, which provides an assessment of the kinematics and ligament strains associated with a candidate alignment. A dataset of eight knee OA patients, including implant positions and the deviations in kinematics and ligament strains between implanted and pre-diseased models, was used to train a multi-task multilayer perceptron (MLP). One MLP was trained per patient to predict implant position quality as quantified by kinematics and ligament strains. A gradient-based optimization method was then applied to the output of the MLP to identify optimal implant positions. Strong correlations were observed between predicted and computed values across all patients. The gradient-based optimization revealed either a single or multiple optimal positions per patient, depending on the geometry of the loss landscape. This reflects the model's ability to capture regions of biomechanical equivalence, as also observed in the musculoskeletal simulations. This proof-of-principle study was designed for patient-specific modeling and therefore focuses on individual optimization rather than generalization to new patients. This study is a critical step toward using biomechanical modeling for the determination of patient-specific optimal implant alignment during TKA in a clinical setting. (Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Implant alignment; Musculoskeletal modeling; Surrogate modeling; Total knee arthroplasty |
| Entry Date(s): | Date Created: 20260219 Date Completed: 20260807 Latest Revision: 20260911 |
| Update Code: | 20260911 |
| DOI: | 10.1016/j.jbiomech.2026.113214 |
| PMID: | 41712980 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-2380 |
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| DOI: | 10.1016/j.jbiomech.2026.113214 |