i-Dent: A virtual assistant to diagnose rare genetic dental diseases.
Deep learning
Dental age assessment
Mask R–CNN
Medical diagnosis
Panoramic radiograph processing
Rare genetic diseases
Teeth segmentation
U-Net
Journal
Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250
Informations de publication
Date de publication:
02 Aug 2024
02 Aug 2024
Historique:
received:
06
02
2024
revised:
16
07
2024
accepted:
18
07
2024
medline:
4
8
2024
pubmed:
4
8
2024
entrez:
3
8
2024
Statut:
aheadofprint
Résumé
Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant and disabling for the patient. Early detection and rapid management are therefore essential in this context. The i-Dent project aims to supply a pre-diagnostic tool to detect rare diseases with tooth agenesis of varying severity and pattern. To identify missing teeth, image segmentation models (Mask R-CNN, U-Net) have been trained for the automatic detection of teeth on patients' panoramic dental X-rays. Teeth segmentation enables the identification of teeth which are present or missing within the mouth. Furthermore, a dental age assessment is conducted to verify whether the absence of teeth is an anomaly or a characteristic of the patient's age. Due to the small size of our dataset, we developed a new dental age assessment technique based on the tooth eruption rate. Information about missing teeth is then used by a final algorithm based on the agenesis probabilities to propose a pre-diagnosis of a rare disease. The results obtained in detecting three types of genes (PAX9, WNT10A and EDA) by our system are very promising, providing a pre-diagnosis with an average accuracy of 72 %.
Identifiants
pubmed: 39096608
pii: S0010-4825(24)01012-6
doi: 10.1016/j.compbiomed.2024.108927
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108927Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.