Advancements of artificial intelligence algorithms in predicting dental implant prognosis from radiographic images: A systematic review.

Abstract

Statement of problem: The ability of artificial intelligence (AI) to accurately forecast the prognosis of dental implants from radiographic images is unclear.

Purpose: The purpose of this systematic review was to evaluate the efficacy of AI algorithms in predicting implant outcomes by focusing on key factors like peri-implantitis, implant stability, marginal bone levels, dental implant failure, implant success, and osseointegration.

Material and methods: This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidelines. The included studies focused on the radiographic data of patients with dental implants where AI algorithms were compared with expert judgment. A comprehensive search in 4 databases and a manual search were conducted. The quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool.

Results: Of 424 references, 13 eligible articles were included. These studies used different radiographic types and AI models. AI algorithms showed promising accuracy rates, reaching 99.8%. Sensitivity and specificity ranged from 67% to 95% and 78% to 100%, respectively. The studies indicated that AI models significantly reduce analysis time compared with manual methods.

Conclusions: AI algorithms demonstrate promising accuracy in predicting dental implant prognosis, enhancing treatment planning, and early intervention. However, variations in AI models and methodologies highlight the need for further research.