Abstract
We present OrthoAI v2, the second iteration of our open-source pipeline for AI-assisted orthodontic treatment planning with clear aligners, substantially extending the single-agent framework previously introduced. The first version established a proof-of-concept based on Dynamic Graph Convolutional Neural Networks (\dgcnn{}) for tooth segmentation but was limited to per-tooth centroid extraction, lacked landmark-level precision, and produced a scalar quality score without staging simulation. \vtwo{} addresses all three limitations through three principal contributions: (i)~a second agent adopting the Conditioned Heatmap Regression Methodology (\charm{})~\cite{rodriguez2025charm} for direct, segmentation-free dental landmark detection, fused with Agent~1 via a confidence-weighted orchestrator in three modes (parallel, sequential, single-agent); (ii)~a composite six-category biomechanical scoring model (biomechanics \times 0.30 + staging \times 0.20 + attachments \times 0.15 + IPR \times 0.10 + occlusion \times 0.10 + predictability \times 0.15) replacing the binary pass/fail check of v1; (iii)~a multi-frame treatment simulator generating F = A \times r temporally coherent 6-DoF tooth trajectories via SLERP interpolation and evidence-based staging rules, enabling ClinCheck 4D visualisation. On a synthetic benchmark of 200 crowding scenarios, the parallel ensemble of OrthoAI v2 reaches a planning quality score of 92.8 \pm 4.1 vs.\ 76.4 \pm 8.3 for OrthoAI v1, a +21\% relative gain, while maintaining full CPU deployability (4.2 \pm 0.8~s).
