MOSAIC aims to determine whether oro-dental morphological anomalies, particularly palatal morphology, associated with rare bone and cartilage diseases can be precisely characterized using 3D digital models analysed through geometric morphometrics. The study will also evaluate whether these morphological signatures can train an artificial intelligence (AI) algorithm to classify syndromes. A prospective monocentric case-control cohort will be constituted, including 3D intra-oral scans and associated clinical data. The final goal is to improve diagnostic accuracy and reduce diagnostic delay in rare bone disorders.
For cases: Diagnosis of a rare bone and cartilage disorder confirmed by the Rare Disease Competence Center for Constitutional Bone Disorders (MOC) or Calcium and Phosphate Metabolism Disorders (CaP), genetically and/or clinically.
Ability to undergo a 3D intra-oral scan;
Ability of the participant to understand the information notice provided regarding the use of their medical data and 3D digital models for research purposes, and to express informed non-objection to participation in the research.
For controls: healthy adults recruited in the Dental Medicine Department.
History of major orthodontic/orthognathic treatment;
Craniofacial conditions unrelated to the studied diseases (e.g., cleft palate, non-target craniofacial syndromes);
Impossibility to obtain a 3D optical impression;
Refusal or inability of the participant to understand the information notice and/or to express informed non-objection to participation in the research.
Parallel
intra-oral 3D optical impression
Other interventionParticipants will undergo a single visit including an intra-oral 3D optical impression and collection of clinical/genetic data
Discriminative ability of geometric morphometric analysis
Discriminative ability of geometric morphometric analysis to differentiate patient subgroups and healthy controls (procMANOVA on Procrustes coordinates, pairwise comparison of Procrustes distance).
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University Hospital, Bordeaux
Lead sponsor
UMR 1026 BioTis
Collaborator
UMR 5199 PACEA
Collaborator
UMR 5259 LAMCOS
Collaborator
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