A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. (December 2021)

• Record Type: Journal Article
• Authors: Shaheen, Eman; Leite, André; Alqahtani, Khalid Ayidh; Smolders, Andreas; Van Gerven, Adriaan; Willems, Holger; Jacobs, Reinhilde
• Is Part Of: Journal of dentistry. Volume 115(2021)
• Publication Date: 2021-12
• Subjects: Cone-beam computed tomography -- Deep learning -- Artificial intelligence -- Neural network models -- Three-dimensional imaging -- Teeth; Dentistry -- Periodicals; Dentistry -- Periodicals; Dentisterie -- Périodiques; Electronic journals; 617.6005
• DOI: 10.1016/j.jdent.2021.103865
• Languages: English
• ISSNs: 0300-5712

Abstract

Abstract: Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images. Methods: A dataset of 186 CBCT scans was acquired from two CBCT machines with different acquisition settings. An artificial intelligence (AI) framework was built to segment and classify teeth. Teeth were segmented in a three-step approach with each step consisting of a 3D U-Net and step 2 included classification. The dataset was divided into training set (140 scans) to train the model based on ground-truth segmented teeth, validation set (35 scans) to test the model performance and test set (11 scans) to evaluate the model performance compared to ground-truth. Different evaluation metrics were used such as precision, recall rate and time. Results: The AI framework correctly segmented teeth with optimal precision (0.98±0.02) and recall (0.83±0.05). The difference between the AI model and ground-truth was 0.56±0.38 mm based on 95% Hausdorff distance confirming the high performance of AI compared to ground-truth. Furthermore, segmentation of all the teeth within a scan was more than 1800 times faster for AI compared to that of an expert. Teeth classification also performed optimally with a recall rate of 98.5% and precision of 97.9%. Conclusions: The proposed 3D U-Net based AI framework is an accurate and time-efficient deep learning system for automatic tooth segmentation and classification without expert refinement. Clinical significance: The proposed system might enable potential future applications for diagnostics and treatment planning in the field of digital dentistry, while reducing clinical workload.