Patient-Specific Optimization for Mandibular Reconstruction Planning with Enhanced Bone Union

2026-05-01

AI Summary

This study addresses the challenge of poor bone healing in vascularized mandibular bone grafting, often caused by suboptimal donor–host bone apposition, which current virtual surgical planning approaches fail to resolve as they focus solely on geometric alignment while neglecting biological healing conditions. To bridge this gap, we propose the first patient-specific optimization framework explicitly designed to enhance bone healing, establishing a closed-loop pipeline from preoperative CT scans to personalized surgical decisions. Our method integrates digital twin modeling, template-to-patient registration, and CT-derived updates of masticatory muscle and temporomandibular joint parameters, optimizing osteotomy planes and donor configurations through a bone apposition–driven objective function regularized by safety constraints. Leveraging Bayesian optimization with an expected improvement–augmented acquisition function, our approach achieves a 329% relative improvement in bone apposition on generic defect models and a 26-percentage-point gain over actual clinical surgeries in patient cases, with predicted apposition correlating strongly (Dice = 0.70–0.76) with one-year postoperative bone formation. The code is publicly released.

Abstract

Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and current virtual surgical planning produces a geometric plan rather than a configuration that explicitly promotes bone union. We present OsteoOpt++, an image-to-decision planning loop for patient-specific mandibular reconstruction. A pre-operative computed tomography (CT) is converted into a personalized digital twin through template-to-patient registration and CT-derived updates of the muscle and temporomandibular-joint parameters. Bayesian optimization with an expected-improvement-plus acquisition rule then searches six clinically controllable cut-plane and donor-positioning variables under an apposition-driven objective and a safety-factor-regularized variant. The workflow was evaluated on three generic defects (body, symphysis, and ramus-body) and a total of 3+1 patient-specific cases, with 3 used for optimization and 1 for validation. In the generic cases, against a common surgical approach, cycle-averaged donor-mandible apposition increased by up to 29 percentage points (329% relative); in the patient-specific cases, against the surgeon-implemented day-5 post-operative configuration, by up to 26 percentage points. A 10% sensitivity analysis over eleven modeling parameters capped the change in the apposition-driven objective at 3% for generic cases and 4% for patient-specific cases, and the longitudinal case showed Dice overlap of 0.70 and 0.76 between predicted apposition and year-1 bone formation. Clinically, this provides surgeons with a pre-operative, image-driven recommendation for cut-plane orientation and donor placement that is predicted to improve union conditions over the configurations currently delivered in the operating room. The optimization and patient-specific modeling code is open source at https://github.com/hamidreza-aftabi/OsteoOpt.

Problem

• mandibular reconstruction
• bone union
• donor-host nonunion
• surgical planning
• bone graft

Innovation

• patient-specific optimization
• bone union
• Bayesian optimization
• digital twin
• mandibular reconstruction