Keywords
Dental implants
Bone resorption
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Abstract
Objective: To compare the use of artificial intelligence (AI) models to detect bone resorption and peri-implantitis in patients with dental implants using imaging exams. Methods: This is a systematic review that followed the PRISMA protocol, searching indexed databases (Scopus, Web of Science, PubMed/MEDLINE, SciELO, Google Scholar, and IEEE Xplore) over the last 10 years. Duplicates were removed, and the articles were screened by titles, abstracts, and full text. Results: Sixteen studies met the criteria; most used convolutional neural networks (CNNs) for automatic implant segmentation and bone loss measurement, with accuracy between 70% and 97%. Machine learning models with clinical or radiological data showed variable performance. Final considerations: AI techniques demonstrate high accuracy in detecting peri-implant bone resorption, although generalization is limited by the size of the databases, methodological heterogeneity, and the need for standardization of protocols.
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