Application of artificial intelligence in dental implant prognosis: A scoping review

Abstract

Objectives: The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants.

Data: Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies.

Sources: This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed.

Study selection: Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %.

Conclusions: AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis.

Clinical significance: AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.

Keywords: Artificial intelligence; Deep learning; Dental implants; Machine learning; Peri-implantitis; Prognosis; Review.