Grant: Alliance Grants
École Polytechnique de Montréal
$243,078.00
Deep learning approach to generate patient-specific teeth_x000d_ _x000d_
EF 2020-21
Résumé
Dental offices are faced with hundreds of thousands of dental reconstructions per year. Each dental reconstruction typically requires a dental professional to manually design and input the characteristics of the tooth to be reconstructed. Consequently, this time-consuming process is difficult to reproduce between professionals and hence leads to great variability in quality. This project will use Deep Learning approaches to develop a new methodology that automatically designs patient-specific teeth. To achieve this goal, we collaborate with industrial partners that will provide us with roughly five thousand cases of digitalized dental impressions for upper and lower dental arches (i.e. mandible and maxilla). Using these healthy arches as a gold standard, we plan to train neural networks to generate and or deform mesh models to yield a volumetric surface representing the tooth to be reconstructed in its spatial context. The resulting integrated system will be designed to continuously learn. Indeed, teeth generated by the system can be modified by a dental professional making a restoration. The resulting modification will then be used to retrain the network and increase its effectiveness.
_x000d_Les cabinets dentaires font face à des centaines de milliers de reconstructions dentaires par an. Chaque reconstruction dentaire nécessite généralement l'intervention d'un professionnel de la santé dentaire pour concevoir et saisir manuellement les caract
Programme
Alliance Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
549122-2019
Name nom
Guibault, François
Department département
Génie informatique et génie logiciel
Organization id
64
Institution établissement
École Polytechnique de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
243078
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1901
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical technology
Research subject fr
Technologie biomédicale
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
Deep learning approach to generate patient-specific teeth_x000d_
_x000d_
Keyword
NSERC
Application summary
Dental offices are faced with hundreds of thousands of dental reconstructions per year. Each dental reconstruction typically requires a dental professional to manually design and input the characteristics of the tooth to be reconstructed. Consequently, this time-consuming process is difficult to reproduce between professionals and hence leads to great variability in quality. This project will use Deep Learning approaches to develop a new methodology that automatically designs patient-specific teeth. To achieve this goal, we collaborate with industrial partners that will provide us with roughly five thousand cases of digitalized dental impressions for upper and lower dental arches (i.e. mandible and maxilla). Using these healthy arches as a gold standard, we plan to train neural networks to generate and or deform mesh models to yield a volumetric surface representing the tooth to be reconstructed in its spatial context. The resulting integrated system will be designed to continuously learn. Indeed, teeth generated by the system can be modified by a dental professional making a restoration. The resulting modification will then be used to retrain the network and increase its effectiveness.
_x000d_Les cabinets dentaires font face à des centaines de milliers de reconstructions dentaires par an. Chaque reconstruction dentaire nécessite généralement l'intervention d'un professionnel de la santé dentaire pour concevoir et saisir manuellement les caract