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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.<\/jats:p>","DOI":"10.1038\/s41598-024-64609-0","type":"journal-article","created":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T19:01:23Z","timestamp":1718564483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning"],"prefix":"10.1038","volume":"14","author":[{"given":"Mohammed","family":"Al-Asali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Yaseen","family":"Alqutaibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Sarem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,16]]},"reference":[{"key":"64609_CR1","doi-asserted-by":"publisher","first-page":"646383","DOI":"10.3389\/fmats.2021.646383","volume":"8","author":"SR Kandavalli","year":"2021","unstructured":"Kandavalli, S. 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