ArticleFrontiers in medicine2026
Machine learning prediction of post-traumatic osteoarthritis based on three-dimensional printing-derived joint congruence biomechanics in ankle fractures.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Post-traumatic osteoarthritis (PTOA) develops in 20-40% of patients following ankle fracture fixation despite anatomic reduction. This study aimed to develop a machine learning model incorporating three-dimensional (3D) printing-derived joint congruence biomechanics for individualized PTOA prediction. Methods: This retrospective cohort study included 263 patients (January 2020-January 2024) who underwent preoperative 3D-printed model-assisted surgical planning with minimum 24-month follow-up. Finite element analysis quantified joint congruence parameters including peak contact pressure, pressure inhomogeneity index, and contact center offset. Four machine learning algorithms were developed using training data ( Results: PTOA developed in 102 patients (38.8%) during mean 36.4-month follow-up. Patients with PTOA demonstrated significantly higher peak contact pressure (15.2 ± 3.8 vs. 9.6 ± 2.4 MPa, Conclusion: Joint congruence parameters from 3D printing-based finite element analysis significantly improve machine learning prediction of PTOA following ankle fracture, identifying high-risk patients even after anatomic reduction.
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