Statement of problem Automated detection of dental caries could enhance early detection,
save clinician time, and enrich treatment decisions. However, a reliable system is lacking …

Objectives Detecting caries lesions is challenging for dentists, and deep learning models
may help practitioners to increase accuracy and reliability. We aimed to systematically …

Objectives: Deep learning methods have achieved impressive diagnostic performance in the
field of radiology. The current study aimed to use deep learning methods to detect caries …

Purpose The aim of this study was to analyse and review deep learning convolutional neural
networks for detecting and diagnosing early-stage dental caries on periapical radiographs …

Objective This study aimed to evaluate a deep learning (DL) system using convolutional
neural networks (CNNs) for automatic detection of caries on bitewing radiographs. Study …

Objectives The aim of this study is to recommend an automatic caries detection and
segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental …

Objectives: We aimed to assess the impact of an artificial intelligence (AI)-based diagnostic-
support software for proximal caries detection on bitewing radiographs. Methods: A cluster …

This study aims to investigate the effect of using an artificial intelligence (AI) system
(Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam …

The study aims to evaluate the diagnostic performance of an artificial intelligence system
based on deep learning for the segmentation of occlusal, proximal and cervical caries …

Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay.
However, traditional methods of diagnosing caries often fall short in accuracy and efficiency …