Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.

OBJECTIVES

This systematic review aimed to assess the effectiveness of Artificial Intelligence (AI)-based Deep Learning (DL) models in the detection of dental caries on intraoral images.

METHODS

This systematic review adhered to PRISMA 2020 guidelines conducting an electronic search on PubMed, Scopus, and CENTRAL databases for retrospective, prospective, and cross-sectional studies published till 1st June 2024. Methodological and performance metrics of clinical studies utilizing DL models were assessed. A modified QUADAS risk of bias tool was used for quality assessment.

RESULTS

Out of 273 studies identified, a total of 23 were included with 19 studies having a low risk and 4 studies having a high risk of bias. Overall accuracy ranged from 56% to 99.1%, sensitivity ranged from 23% to 98% and specificity ranged from 65.7% to 100%. Only 3 studies utilized explainable AI (XAI) techniques for caries detection. A total of 4 studies exhibited a level 4 deployment status by developing mobile or web-based applications.

CONCLUSION

AI-based DL models have demonstrated promising prospects in enhancing the detection of dental caries, especially in terms of low-resource settings. However, there is a need for future deployed studies to enhance the AI models to improve their real-world applications.