Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study

• Published in: Journal of dentistry 2023-10, Vol.137, p.104639-104639, Article 104639
• Main Authors: Elgarba, Bahaaeldeen M., Van Aelst, Stijn, Swaity, Abdullah, Morgan, Nermin, Shujaat, Sohaib, Jacobs, Reinhilde
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Computer neural networks; Deep learning; Dental implant and cone-beam computed tomography; Machine learning; Medicin och hälsovetenskap
• ISSN: 0300-5712; 1879-176X; 1879-176X
• DOI: 10.1016/j.jdent.2023.104639

To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images. A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30). A CNN model was developed and trained using expert-based semi-automated segmentation (SS) of the implant and attached prosthetic crown as the ground truth. The performance of AS was assessed by comparing with SS and manually corrected automated segmentation referred to as refined-automated segmentation (R-AS). Evaluation metrics included timing, voxel-wise comparison based on confusion matrix and 3D surface differences. The average time required for AS was 60 times faster (