OrthoAI v2: From Single-Agent Segmentation to Dual-Agent Treatment Planning for Clear Aligners
arXiv:2603.15663v1 Announce Type: new
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).
