Objectives: To investigate the current clinical applications and diagnostic performance of
artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). Methods: Studies …

Background With the advent of deep learning in modern computing there has been
unprecedented progress in image processing and segmentation. Deep learning-based …

ABSTRACT Purpose: Artificial intelligence (AI), represented by deep learning, can be used
for real-life problems and is applied across all sectors of society including medical and …

Background Dental plaque causes many common oral diseases (eg, caries, gingivitis, and
periodontitis). Therefore, plaque detection and control are extremely important for children's …

Localization of region of interest (ROI) is paramount to the analysis of medical images to
assist in the identification and detection of diseases. In this research, we explore the …

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic
tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive …

Aim To estimate the automated biofilm detection capacity of the U‐Net neural network on
tooth images. Materials and Methods Two datasets of intra‐oral photographs taken in the …

Importance Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and
eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining …

Artificial intelligence and deep learning algorithms are infiltrating various fields of medicine
and dentistry. The purpose of the current study was to review literatures applying deep …

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to
screen for health conditions. We report an automated process that combines intraoral …