Automatic placement of simulated dental implants within CBCT images in optimum positions: a deep learning model

• Published in: Medical & biological engineering & computing Vol. 63; no. 8; pp. 2325 - 2339
• Main Authors: Alotaibi, Shahd, Alsomali, Mona, Alghamdi, Shatha, Alfadda, Sara, Alturaiki, Isra, Al-Ekrish, Asma’a, Altwaijry, Najwa
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025; Springer Nature B.V
• Subjects: Accuracy; Artificial intelligence; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Bone density; Classification; Computed tomography; Computer Applications; Cone-Beam Computed Tomography - methods; Datasets; Deep Learning; Dental Implantation - methods; Dental Implants; Dental prosthetics; Dentistry; Dentists; Human Physiology; Humans; Imaging; Imaging, Three-Dimensional - methods; Jaw; Medical diagnosis; Medical research; Neural networks; Original Article; Planning; Radiology; Software; Teeth; Three dimensional imaging; Tomography; Deep learning; Regression; Computer vision; Classification; CBCT; Image detection
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-025-03327-9

Implant dentistry is the standard of care for the replacement of missing teeth. It is a complex process where cone-beam computed tomography (CBCT) images are analyzed by the dentist to determine the implants’ length, diameter, and position, and angulation diameter, position, and angulation taking into consideration the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. This traditional procedure is time-consuming and relies heavily on the dentist’s knowledge and expertise, which makes it subject to human errors. This study presents a two-stage framework for the placement of dental implants. The first stage utilizes YOLOv11 for the detection of fiducial markers and adjacent bone within 2D slices of 3D CBCT images. In the second stage, classification and regression are applied to extract the apical and occlusal coordinates of the implants and to predict the implants’ intra-osseous length and intra-osseous diameter. YOLOv11 achieved a 59% F-score in the marker detection phase. The mean absolute error for the implant position prediction ranged from 11.931 to 15.954. The classification of the intra-osseous diameter showed 76% accuracy, and the intra-osseous length showed an accuracy of 59%. Our results were reviewed by an expert prosthodontist and deemed promising. Graphical abstract