Related Experiment Video
Updated: Sep 12, 2025
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Application of artificial intelligence in bone quality and quantity assessment for dental implant planning: A scoping
Shuo Qiu1, Xinbo Yu1, Yiqun Wu1
1Second 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.
Objectives:
To assess how artificial intelligence (AI) models perform in evaluating bone quality and quantity in the preoperative planning process for dental implants.
Data:
This review included studies that utilized AI-based assessments of bone quality and/or quantity based on radiographic images in the preoperative phase.
Sources:
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.
Study Selection:
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 %).
Conclusions:
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.
Clinical Significance:
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.
