Towards patient-specific optimization for mandibular reconstruction planning based on predicted bone-union propensity

The paper introduces OsteoOpt++, an image‑to‑decision loop that uses pre‑operative CT scans to build a personalized digital twin of a patient’s mandible and then applies Bayesian optimization to adjust six surgical variables for improved bone‑union propensity at the donor‑host interface. In both generic defect models and patient‑specific cases, the optimized plans increased donor‑mandible apposition by up to 29 % and 26 % respectively compared to surgeon‑generated plans, and the predicted apposition closely matched year‑1 bone formation (Dice overlap 70–85 %). The study demonstrates the feasibility of using apposition‑derived predictions to evaluate and compare reconstruction options and provides open‑source code for further development.

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