Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning

Al-Asali, Mohammed and Alqutaibi, Ahmed Yaseen and Al-Sarem, Mohammed and Saeed, Faisal (2024) Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning. Scientific Reports, 14 (1). ISSN 2045-2322

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Abstract

Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.

Item Type:	Article
Identification Number:	10.1038/s41598-024-64609-0
Dates:	Date Event 11 June 2024 Accepted 16 June 2024 Published Online
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	05 Sep 2024 15:40
Last Modified:	05 Sep 2024 15:40
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15695

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