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 …

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

This study aimed to evaluate the validity of a deep learning-based convolutional neural
network (CNN) for detecting proximal caries lesions on bitewing radiographs. A total of 978 …

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: 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 …

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 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: 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 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 convolutional neural networks (CNNs) are a rapidly emerging new area of
medical research, and have yielded impressive results in diagnosis and prediction in the …