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 …

The early detection of initial dental caries enables preventive treatment, and bitewing
radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the …

Objectives This study aimed to investigate the effectiveness of deep convolutional neural
network (CNN) in the diagnosis of interproximal caries lesions in digital bitewing …

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 …

Dental caries is an extremely common problem in dentistry that affects a significant part of
the population. Approximal caries are especially difficult to identify because their position …

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 …

The aim of this study was to evaluate the diagnostic reliability of a web-based artificial
intelligence program for the detection of interproximal caries in bitewing radiographs. Three …

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: 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 …

Objectives We aimed to apply deep learning to detect caries lesions of different radiographic
extension on bitewings, hypothesizing it to be significantly more accurate than individual …