Evidence-based dentistry · 2024
Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Noor Uddin Ayesha, Ali Syed Ahmed, Lal Abhishek, Adnan Niha, Ahmed Syed Muhammad Faizan, Umer Fahad
Аннотация
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.
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.