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

Mohammed Al-Asali, Mohammed Al-Sarem, Ahmed Yaseen Alqutaibi (Corresponding / Lead Author), Faisal Saeed (Corresponding / Lead Author)

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    Article number13888
    Number of pages12
    JournalScientific Reports
    Volume14
    Issue number1
    DOIs
    Publication statusPublished (VoR) - 16 Jun 2024

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