Deep learning models trained on real clinical data. Tooth movement prediction, automated segmentation, cephalometric analysis. Peer-reviewed research powering clinical decisions.
Each model addresses a specific clinical challenge in orthodontic treatment planning.
Multi-task neural network predicting required tooth movements from pre-treatment 3D scans. Predicts both movement type (rotation, intrusion, expansion) and magnitude.
Published in IEEE TMI. Automatically labels individual teeth from intraoral scans. 14 teeth + gingiva segmentation from raw 3D meshes.
AI landmark detection on X-rays and facial photos. 19 anatomical landmarks, skeletal angles (SNA, SNB, ANB), and dental measurements.
Deep learning embeddings from 3D scans enable semantic similarity search. Find past cases with similar malocclusions for treatment reference.
Predict treatment duration, complexity scores, and expected outcomes. Data-driven insights from real clinical results.
3D intraoral scans (STL), CBCT volumes, clinical photos, and treatment metadata from practicing orthodontists.
Multi-modal deep learning with data augmentation, cross-validation, and biological constraint enforcement.
Trained models are deployed in OrthoAssist AI Pro, providing real-time predictions and treatment guidance.
Every prediction is grounded in peer-reviewed orthodontic research and validated against clinical outcomes.
MeshSegNet published in IEEE Transactions on Medical Imaging + MICCAI 2019
Movement limits based on Kravitz, Haouili, Simon et al. research
Predictability rates validated: 72% mesial-distal, 47% bucco-lingual, 30% extrusion
Multi-task learning: simultaneous classification and regression on tooth movements
Kravitz et al. (2009) — How well does Invisalign work? A prospective clinical study evaluating the efficacy of tooth movement with Invisalign
AJO-DO · PMID: 19154919
Haouili et al. (2020) — Has Invisalign improved? A prospective follow-up study evaluating the efficacy of tooth movement with Invisalign
AJO-DO · PMID: 32241352
Simon et al. (2014) — Treatment outcome and efficacy of an aligner technique regarding incisor torque, premolar derotation and molar distalization
BMC Oral Health · PMID: 24352707
Weltman et al. (2010) — Root resorption associated with orthodontic tooth movement: A systematic review
AJO-DO · PMID: 20197161
Lian et al. (2020) — Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces
IEEE TMI, Vol. 39, No. 7 · DOI: 10.1109/TMI.2020.2971730
Lian et al. (2019) — MeshSegNet: Deep Multi-scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces
MICCAI 2019, LNCS vol 11769 · Springer
Feng et al. (2018) — Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
CVPR 2018
Proffit WR, Fields HW, Sarver DM (2018) — Contemporary Orthodontics, 6th Edition
Elsevier · ISBN: 978-0-323-54387-3
Nelson SJ (2014) — Wheeler's Dental Anatomy, Physiology, and Occlusion, 10th Edition
Elsevier · ISBN: 978-0-323-26323-8
Deep multi-scale mesh feature learning for automated tooth labeling
Chunfeng Lian et al.
Point cloud processing architecture for 3D deep learning
Charles R. Qi et al.
VTK-based 3D visualization and mesh processing toolkit
Marco Musy
RAG framework for clinical knowledge retrieval
Jerry Liu et al.
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