Amanda Ding | Searcharxiv

arXiv2026

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

Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and virtual surgical planning produces a geometric plan rather than optimizing for bone-union propensity at the donor-host interface. We present OsteoOpt++, an image-to-decision planning loop for patient-specific mandibular reconstruction. 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 four patient-specific cases, three of which were used for optimization and all four for retrospective longitudinal spatial analysis. In the generic cases, against the surgeon's geometric plan, cycle-averaged donor-mandible apposition increased by up to 29 percentage points; in the patient-specific cases, against the surgeon-implemented day-5 postoperative configuration, by up to 26 percentage points. A +/-10% sensitivity analysis over eleven modeling parameters capped the change in the apposition-driven objective at approximately 3% (generic) and approximately 4% (patient-specific), and across the four longitudinal cases the Dice overlap between predicted apposition and year-1 bone formation ranged from 70.1% to 84.9%, with centroid shifts of 0.24 to 1.82 mm. Together, these results support the feasibility-stage use of OsteoOpt++ to compare candidate reconstructions using apposition-derived predictions of bone-union propensity. The optimization and patient-specific modeling code is open source at https://github.com/hamidreza-aftabi/OsteoOpt.

cs.CV↗

arXiv2024

Enhancing Table Representations with LLM-powered Synthetic Data Generation

In the era of data-driven decision-making, accurate table-level representations and efficient table recommendation systems are becoming increasingly crucial for improving table management, discovery, and analysis. However, existing approaches to tabular data representation often face limitations, primarily due to their focus on cell-level tasks and the lack of high-quality training data. To address these challenges, we first formulate a clear definition of table similarity in the context of data transformation activities within data-driven enterprises. This definition serves as the foundation for synthetic data generation, which require a well-defined data generation process. Building on this, we propose a novel synthetic data generation pipeline that harnesses the code generation and data manipulation capabilities of Large Language Models (LLMs) to create a large-scale synthetic dataset tailored for table-level representation learning. Through manual validation and performance comparisons on the table recommendation task, we demonstrate that the synthetic data generated by our pipeline aligns with our proposed definition of table similarity and significantly enhances table representations, leading to improved recommendation performance.

cs.LG↗