OrthoAI: A Neurosymbolic Framework for Evidence-Grounded Biomechanical Reasoning in Clear Aligner Orthodontics

Edouard Lansiaux; Margaux Leman; Mehdi Ammi

cs.CVFeb 23, 2026

OrthoAI: A Neurosymbolic Framework for Evidence-Grounded Biomechanical Reasoning in Clear Aligner Orthodontics

Authors: Edouard Lansiaux, Margaux Leman, Mehdi Ammi

Organizations: STaR-AI, Emergency Department, Lille University Hospital, 59000, Lille, France · Artificial Intelligence and Data Semantics Laboratory, Paris 8 University, 93200, Saint-Denis, France

Abstract

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.

Figures & tables

Movement	Tooth class	Limit	Type	Source
Bodily translation ( $t_{x},t_{y}$ )	All	0.25 mm	Soft	[ 11 ]
Intrusion ( $t_{z}<0$ )	All	0.25 mm	Soft	[ 2 ]
Extrusion ( $t_{z}>0$ )	Posterior	0.20 mm	Hard	[ 1 ]
Extrusion ( $t_{z}>0$ )	Anterior	0.15 mm	Hard	[ 1 ]
Mesiodistal tip ( $r_{y}$ )	All	0.25 mm eq.	Soft	[ 11 ]
Axial rotation ( $r_{z}$ )	Canine	2.0°	Soft	[ 2 ]

Table 1: Orthodontic CSP constraint specification. Domain limits

\bar{v}_{k,j}

by movement type and tooth class. Hard violation threshold

\alpha=1.5

Panel of Figure 1 — Figure 1: DGCNN_Seg architecture with receptive field interpretation. Each EdgeConv layer targets a different geometric scale (annotations below). Global max-pooling resolves arch-level symmetry ambiguities. Total: 60,705 parameters.

Model	mIoU	tIoU	Acc	TIR
DGCNN_Seg (ours, full)	0.0825	0.0617	0.345	0.814
– batch-adaptive Dice	0.0612	0.0481	0.302	0.743
– label smoothing	0.0781	0.0590	0.338	0.802
– global pooling	0.0644	0.0503	0.297	0.671
– multi-scale concat	0.0710	0.0541	0.318	0.758
PointNet (baseline)	0.0423	0.0311	0.228	0.612

Table 2: Segmentation results on the 3DTeethLand validation set (20 scans).

$k$	mIoU	TIR	Acc	Train loss
3	0.0825	0.814	0.345	2.152
5	0.0793	0.801	0.331	2.193
10	0.0718	0.776	0.312	2.267
20	0.0641	0.744	0.288	2.401

Table 3: Ablation over EdgeConv neighborhood size

k

. Training on 80 scans; best epoch reported.

Feature set	mIoU	TIR	Acc
Full 6-dim (ours)	0.0825	0.814	0.345
– arch-centroid dist ( $\\|\tilde{p}\\|$ )	0.0764	0.746	0.319
– height ( $\tilde{p}_{z}$ , duplicate)	0.0812	0.808	0.341
– radial dist ( $\sqrt{\tilde{p}_{x}^{2}+\tilde{p}_{y}^{2}}$ )	0.0791	0.783	0.332
XYZ only (3-dim)	0.0638	0.699	0.278

Table 4: Feature engineering ablation. Each row removes one dimension from the 6-dim feature vector.

Loss	mIoU	TIR	Acc	Convergence
CE only	0.0631	0.724	0.301	Stable
CE + FD	0.0711	0.769	0.328	Unstable (rare classes)
CE + BD (no LS)	0.0781	0.802	0.338	Stable
CE (LS) + BD (ours)	0.0825	0.814	0.345	Stable

Table 5: Loss function ablation. CE: cross-entropy; LS: label smoothing; BD: batch-adaptive Dice; FD: full-batch Dice.

Panel of Figure 2 — Figure 2: Validation metrics over 10 epochs. TIR and mIoU exhibit divergent optimization trajectories, empirically validating the perception-reasoning decoupling hypothesis (Section 3.2 ).

Panel of Figure 3 — Figure 3: Training loss curve over 10 epochs. Monotonic decrease from 3.17 to 2.15 confirms stable convergence.

Patient	Teeth	WAVF Score	Grade	Critical	Warning	Est. Duration
01WU1B7C	16	79	B	2	3	12 mo
13GRE9RZ	14	82	B	1	2	10 mo
3JIYO434	14	77	B	2	2	11 mo
233LFZAG	14	84	B	1	1	0 9 mo
Mean	14.5	80.5	B	1.5	2.0	10.5 mo

Table 6: Biomechanical CSP inference results. Index scale 0–100; grade thresholds: A

\geq

90, B

\geq

75, C

\geq

60.

Perturbed criterion	Max $\Delta S$	Relative $\Delta S$ / $\bar{S}$
Biomechanical compliance ( $w=0.30$ )	$\pm 7.2$ pts	$\pm 9.0\%$
Predictability ( $w=0.20$ )	$\pm 4.8$ pts	$\pm 6.0\%$
Staging efficiency ( $w=0.15$ )	$\pm 3.1$ pts	$\pm 3.9\%$
Attachment planning ( $w=0.15$ )	$\pm 2.9$ pts	$\pm 3.6\%$
IPR indication ( $w=0.10$ )	$\pm 1.8$ pts	$\pm 2.2\%$
Arch symmetry ( $w=0.10$ )	$\pm 1.6$ pts	$\pm 2.0\%$

Table 7: WAVF sensitivity analysis. Score variation (

\Delta S

) under

\pm 50\%

perturbation of individual weights.

System	Params	Supervision	mIoU †	CDSS	Formalism
PointNet++ [ 5 ]	1.0M	Full mesh	$\sim$ 85%	No	None
TSGCNet [ 9 ]	1.5M	Full mesh	$\sim$ 90%	No	None
MeshSegNet [ 6 ]	0.8M	Full mesh	$\sim$ 88%	No	None
ClinCheck (commercial)	N/A	N/A	N/A	Partial	None
OrthoAI (ours)	60.7K	LM only	8.25%	Yes (CSP+MCDA)	Full

Table 8: System-level comparison. “CDSS” indicates clinical decision support capability; “LM” landmark-only supervision; “Full” dense mesh annotations.

\dagger

: not directly comparable (different data and evaluation protocols).

3D Point Cloud Segmentation

3D point cloud segmentation assigns semantic, instance, or part labels to points for scene understanding, mapping, robotics, and scientific measurement.

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Mar 10, 2026Lansiaux Edouard, Leman Margaux
STaR-AI Research Group, Emergency Department, Lille University Hospital
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Apr 1, 2026Kyeonghun Kim, Jaehyung Park, Youngung Han +17Dental Imaging Medical Imaging
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TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
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Shandong University, Jinan 250010, China · University of Surrey, Guildford, Surrey, United Kingdom · King’s College London, London, United Kingdom

Mar 10, 2026J/K move · Enter open · S save

OrthoAI v2: From Single-Agent Segmentation to Dual-Agent Treatment Planning for Clear Aligners

Lansiaux Edouard, Leman Margaux

STaR-AI Research Group, Emergency Department, Lille University Hospital

Open paper AskPDF arXiv

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).

Abstract

3D Point Cloud Segmentation

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