Application of Artificial Intelligence in Bone Quality and Quantity Assessment for Dental Implant Planning: A Scoping Review.
Authors
Affiliations (2)
Affiliations (2)
- Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, China.
- Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, China. Electronic address: [email protected].
Abstract
To assess how artificial intelligence (AI) models perform in evaluating bone quality and quantity in the preoperative planning process for dental implants. This review included studies that utilized AI-based assessments of bone quality and/or quantity based on radiographic images in the preoperative phase. Studies published in English before April 2025 were used in this review, which were obtained from searches in PubMed/MEDLINE, Embase, Web of Science, Scopus, and the Cochrane Library, as well as from manual searches. Eleven studies met the inclusion criteria. Five studies focused on bone quality evaluation and six studies included volumetric assessments using AI models. The performance measures included accuracy, sensitivity, specificity, precision, F1 score, and Dice coefficient, and were compared with human expert evaluations. AI models demonstrated high accuracy (76.2%-99.84%), high sensitivity (78.9%-100%), and high specificity (66.2%-99%). AI models have potential for the evaluation of bone quality and quantity, although standardization and external validation studies are lacking. Future studies should propose multicenter datasets, integration into clinical workflows, and the development of refined models to better reflect real-life conditions. AI has the potential to offer clinicians with reliable automated evaluations of bone quality and quantity, with the promise of a fully automated system of implant planning. It may also support preoperative workflows for clinical decision-making based on evidence more efficiently.