BACKGROUND: To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images.
OBJECTIVES: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH).
OBJECTIVES: The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices.
BACKGROUND: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs.
This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University H...
OBJECTIVES: This study aimed to develop a deep learning (DL) model for the predictive esthetic evaluation of single-implant treatments in the esthetic zone.
OBJECTIVE: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.
Quintessence international (Berlin, Germany : 1985)
39601186
Dental caries is one of the most common diseases globally. It affects children and adults living in poverty, who have the most limited access to dental care. Left unexamined and untreated in the early stages, treatments for late-stage and severe cari...
BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photograp...
BACKGROUND: The aim of our study was to develop and evaluate a deep learning model (BiStageNet) for automatic detection of dens evaginatus (DE) premolars on orthodontic intraoral photographs. Additionally, based on the training results, we developed ...