Automated clinical decision support for clear aligner orthodontics faces a key challenge: bridging geometric perception (3D tooth segmentation) with clinical reasoning (biomechanical feasibility). We address this with OrthOAI, introducing three methodological contributions. First, sparse-supervision segmentation: a landmark-to-point-cloud synthesis protocol enables training from sparse anatomical annotations (6-8 points per tooth) instead of dense labels, combined with a clinically stratified loss mixing label-smoothed cross-entropy and a batch-adaptive Dice term for class imbalance. Second, knowledge-grounded constraint inference: biomechanical feasibility is modeled as a Constraint Satisfaction Problem over a domain ontology of tooth movements, encoding evidence-based per-stage limits as soft and hard constraints. Third, multi-criteria treatment evaluation: treatment quality is scored through a formal Multi-Criteria Decision Analysis framework using a weighted Additive Value Function grounded in clinical priority theory. On landmark-reconstructed point clouds from 3DTeethLand (MICCAI 2024), segmentation reaches 81.4% Tooth Identification Rate with 60,705 parameters. Ablations quantify the impact of each design choice. End-to-end inference runs in under 4 seconds on CPU. We also outline the gap between the current prototype-trained on synthetic ellipsoidal approximations-and clinical deployment, with a roadmap for validation. Code and weights are released.

翻译：隐形矫治正畸的自动化临床决策支持面临一个关键挑战：如何将几何感知（3D牙齿分割）与临床推理（生物力学可行性）相衔接。我们通过OrthOAI应对这一挑战，并引入三项方法论贡献。首先，稀疏监督分割：一种从标志点到点云的合成协议，使得能够基于稀疏解剖标注（每颗牙6-8个点）而非密集标签进行训练，并结合临床分层损失函数，混合了标签平滑交叉熵与针对类别不平衡的批次自适应Dice项。其次，知识驱动的约束推断：生物力学可行性被建模为基于牙齿移动领域本体的约束满足问题，将循证的每阶段移动限制编码为软约束与硬约束。第三，多准则治疗方案评估：治疗方案质量通过一个形式化的多准则决策分析框架进行评分，该框架采用基于临床优先级理论的加权加性价值函数。在来自3DTeethLand（MICCAI 2024）的标志点重建点云上，分割任务以60,705个参数实现了81.4%的牙齿识别率。消融实验量化了每个设计选择的影响。端到端推断在CPU上运行时间低于4秒。我们还阐述了当前基于合成椭球近似训练的模型原型与临床部署之间的差距，并提出了验证路线图。代码与权重已开源。