TY - JOUR
T1 - Applications of AI-based deep learning models for detecting dental caries on intraoral images – a systematic review
AU - Noor Uddin, Ayesha
AU - Ali, Syed Ahmed
AU - Lal, Abhishek
AU - Adnan, Niha
AU - Ahmed, Syed Muhammad Faizan
AU - Umer, Fahad
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to British Dental Association 2024.
PY - 2025/3
Y1 - 2025/3
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105001492864
U2 - 10.1038/s41432-024-01089-1
DO - 10.1038/s41432-024-01089-1
M3 - Article
AN - SCOPUS:105001492864
SN - 1462-0049
VL - 26
SP - 71
EP - 72
JO - Evidence-Based Dentistry
JF - Evidence-Based Dentistry
IS - 1
M1 - 1236
ER -