Enhanced Tooth Region Detection Using Pretrained Deep Learning Models

Al-Sarem, Mohammed and Al-Asali, Mohammed and Alqutaibi, Ahmed Yaseen and Saeed, Faisal (2022) Enhanced Tooth Region Detection Using Pretrained Deep Learning Models. International Journal of Environmental Research And Public Health, 19 (22). p. 15414. ISSN 1660-4601

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The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient’s panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant placement planning to find the correct implant position and eliminate surgical risks. This study aims to develop a deep learning-based model that detects missing teeth’s position on a dataset segmented from CBCT images. Five hundred CBCT images were included in this study. After preprocessing, the datasets were randomized and divided into 70% training, 20% validation, and 10% test data. A total of six pretrained convolutional neural network (CNN) models were used in this study, which includes AlexNet, VGG16, VGG19, ResNet50, DenseNet169, and MobileNetV3. In addition, the proposed models were tested with/without applying the segmentation technique. Regarding the normal teeth class, the performance of the proposed pretrained DL models in terms of precision was above 0.90. Moreover, the experimental results showed the superiority of DenseNet169 with a precision of 0.98. In addition, other models such as MobileNetV3, VGG19, ResNet50, VGG16, and AlexNet obtained a precision of 0.95, 0.94, 0.94, 0.93, and 0.92, respectively. The DenseNet169 model performed well at the different stages of CBCT-based detection and classification with a segmentation accuracy of 93.3% and classification of missing tooth regions with an accuracy of 89%. As a result, the use of this model may represent a promising time-saving tool serving dental implantologists with a significant step toward automated dental implant planning.

Item Type: Article
Identification Number: https://doi.org/10.3390/ijerph192215414
17 November 2022Accepted
21 November 2022Published Online
Uncontrolled Keywords: pretrained deep learning; missing teeth; CBCT; DenseNet169 model; CNNs; image segmentation; U-Net model
Subjects: CAH01 - medicine and dentistry > CAH01-01 - medicine and dentistry > CAH01-01-04 - dentistry
CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 29 Nov 2022 09:35
Last Modified: 29 Nov 2022 09:35
URI: https://www.open-access.bcu.ac.uk/id/eprint/13961

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