Back to Search
Start Over
Convolutional neural network for automated tooth segmentation on intraoral scans.
- Source :
- BMC Oral Health; 7/16/2024, Vol. 24 Issue 1, p1-9, 9p
- Publication Year :
- 2024
-
Abstract
- Background: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propose and evaluate the performance of a convolutional neural network (CNN) model for automatic tooth segmentation on IOS images. Methods: A dataset of 761 IOS images (380 upper jaws, 381 lower jaws) was acquired using an intraoral scanner. The inclusion criteria included a full set of permanent teeth, teeth with orthodontic brackets, and partially edentulous dentition. A multi-step 3D U-Net pipeline was designed for automated tooth segmentation on IOS images. The model's performance was assessed in terms of time and accuracy. Additionally, the model was deployed on an online cloud-based platform, where a separate subsample of 18 IOS images was used to test the clinical applicability of the model by comparing three modes of segmentation: automated artificial intelligence-driven (A-AI), refined (R-AI), and semi-automatic (SA) segmentation. Results: The average time for automated segmentation was 31.7 ± 8.1 s per jaw. The CNN model achieved an Intersection over Union (IoU) score of 91%, with the full set of teeth achieving the highest performance and the partially edentulous group scoring the lowest. In terms of clinical applicability, SA took an average of 860.4 s per case, whereas R-AI showed a 2.6-fold decrease in time (328.5 s). Furthermore, R-AI offered higher performance and reliability compared to SA, regardless of the dentition group. Conclusions: The 3D U-Net pipeline was accurate, efficient, and consistent for automatic tooth segmentation on IOS images. The online cloud-based platform could serve as a viable alternative for IOS segmentation. [ABSTRACT FROM AUTHOR]
- Subjects :
- DENTAL radiography
TOOTH anatomy
DENTAL care
RESEARCH funding
THREE-dimensional imaging
DIGITAL diagnostic imaging
ARTIFICIAL intelligence
DENTITION
DESCRIPTIVE statistics
ORTHODONTIC appliances
WORKFLOW
ARTIFICIAL neural networks
DENTISTRY
DIGITAL image processing
AUTOMATION
MAXILLA
MACHINE learning
COMPARATIVE studies
ORAL health
CLOUD computing
RELIABILITY (Personality trait)
Subjects
Details
- Language :
- English
- ISSN :
- 14726831
- Volume :
- 24
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Oral Health
- Publication Type :
- Academic Journal
- Accession number :
- 178462982
- Full Text :
- https://doi.org/10.1186/s12903-024-04582-2