Artificial Intelligence in Dentistry - A Review

L, Gokul G; S, Balaji Ganesh; Don, K. R.

doi:10.37506/ijfmt.v14i4.12471

Cited by 1 publication

(1 citation statement)

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“…The input set is used for performing various machine learning work like regression as well as grouping. Various forms of AI began to affect dentists, including better radiographic imaging, diagnostic procedures for cysts as well as tumors, diagnosis of periapical lesions, identification of root anatomy and endodontics, diagnosis of periodontitis in place, cephalometric points in orthodontics 13 . Various uses of AI in blood restoration were assessed.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Artificial Intelligence Applications in Restorative Dentistry

Fatima¹,

Akbar²,

Ahmed³

et al. 2023

PJMHS

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Statement of problem: Artificial intelligence (AI) applications are increasingly used in restorative process. But existing dentistry restoration effectiveness as well as growth and IA applications have not yet been systematically analyzed or documented. Purpose: The goal of such comprehensive evaluation is to discover & assess the abilities associated with artificial intelligence models in restorative dentistry for the analysis of caries as well as vertical tooth fracture, evaluate margins in preparing tooth, and analyze reconstructive failures. Methods: A systematic electronic review of 5 databases was carried out: PubMed/ MEDLINE, World of Science, EMBASE, Scopus and Cochrane. The investigation was carried out manually as well. Research using AI models was chosen on the basis of 4 criterion: dental caries diagnostics, diagnostics, vertical tooth fracture, tooth preparation recognition, & cause of failure of restoration. Both researchers rated the quality of the study for Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). The 3rd author was asked for resolving the dispute. Results: 34 researches were made the part of this analysis: from which 29 contains artificial intelligence techniques including the diagnostics or treatment related to caries and its causes according to sensitivity models, 2 to diagnose vertical tooth fractures, 1 to prepare the teeth. Among the studied analysis, the accuracy of caries diagnosis in the AI models was tested from 76-88.3%, sensitivity from 73-90%, as well as specificity from 61.5-93%. In the study, the accuracy of predicted caries ranges from 83.6-97.1%. The performed research showed the accuracy of the analysis of a vertical tooth fracture from 88.3-95.7%. The study, which uses AI models to find a destination, had details with the range of 90.6-97.4%. Conclusions: AI models are a powerful tool for diagnosing caries as well as vertical tooth fractures, recognizing preparation margins and predicting restoration failures. But, the dental use of AI models continues to evolve. More research is needed to evaluate the clinical effectiveness of artificial intelligence models in restorative dentistry. Keyword: Artificial intelligence, restorative dentistry, vertical tooth fractures.

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Section: Introductionmentioning

confidence: 99%