AIMC Topic: Photography, Dental
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FDTooth: Intraoral Photographs and CBCT Images for Fenestration and Dehiscence Detection.
Scientific data
Jun 14, 2025
Fenestration and dehiscence (FD) pose significant challenges in dental treatments as they adversely affect oral health. Although cone-beam computed tomography (CBCT) provides precise diagnostics, its extensive time requirements and radiation exposure...
AI-Driven Detection and Measurement of Keratinized Gingiva in Dental Photographs: Validation Using Reference Retainers.
Journal of clinical periodontology
Apr 7, 2025
AIM: To evaluate a deep learning (DL) model for detecting keratinized gingiva (KG) in dental photographs and validate its clinical applicability using reference retainers for calibration.
A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.
BMC oral health
Mar 29, 2025
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...
Diagnostic accuracy of artificial intelligence for dental and occlusal parameters using standardized clinical photographs.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Mar 3, 2025
INTRODUCTION: SmileMate (SmileMate, Dental Monitoring SAS, Paris, France) is an artificial intelligence (AI)-based Web site that uses intraoral photographs to assess patients' dental and orthodontic parameters and provide a report. This study aimed t...
Data-driven AI platform for dens evaginatus detection on orthodontic intraoral photographs.
BMC oral health
Mar 1, 2025
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 ...
Prediction of pink esthetic score using deep learning: A proof of concept.
Journal of dentistry
Jan 31, 2025
OBJECTIVES: This study aimed to develop a deep learning (DL) model for the predictive esthetic evaluation of single-implant treatments in the esthetic zone.
The application of deep learning in early enamel demineralization detection.
PeerJ
Jan 2, 2025
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.
Developing an AI-based application for caries index detection on intraoral photographs.
Scientific reports
Nov 5, 2024
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...
DeepPlaq: Dental plaque indexing based on deep neural networks.
Clinical oral investigations
Sep 20, 2024
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.
Comparison of deep learning models to detect crossbites on 2D intraoral photographs.
Head & face medicine
Sep 2, 2024
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.
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