Dental Health

Comparison between automated and manual digital diagnostic setups of orthodontic extraction cases: an in silico study.

BACKGROUND: The aim of the study was to evaluate automated digital diagnostic setup in bimaxillary dentoalveolar protrusion cases using two software packages and to compare them to manual digital setup. METHODOLOGY: Pre-treatment intraoral scans of 14 patients whose treatment plans involved extraction of four first premolars were imported as Standard Tessellation Language files into dentOne® softw...

ChatGPT performance in orthodontics : Assessment of accuracy and repeatability in patient instruction and management using Global Quality Score.

PURPOSE: This study assessed the accuracy and repeatability of the orthodontics-related information generated by Chat Generative Pre-trained Transformer (ChatGPT, model GPT-4o, 18 July 2024, OpenAI, San Francisco, CA, USA) and evaluated its usefulness for patient education by comparing artificial intelligence (AI)-generated responses to questions that patients frequently search for with responses ...

Designing Poly(ionic liquid)s as High-Performance LiFePO4 Binders via Mechanistic Study and ML-Assisted Structure-Property Analysis.

Conventional LiFePO4 (LFP) cathodes employing poly(vinylidene fluoride) (PVDF) as binder exhibit relatively stable cycling performance but suffer from...

Accuracy of DentalMonitoring's artificial intelligence in detecting common orthodontic braces treatment related emergencies.

This study aimed to evaluate the diagnostic performance of DentalMonitoring™ (DM) (Dental Monitoring SAS, Paris, France), an FDA-cleared AI-powered or...

Artificial Intelligence-Based Multi-Stage System for Automated Angle's Classification of Malocclusion from Intraoral Images in Orthodontics.

This study presents a multi-stage deep learning pipeline for automated Angle's classification of occlusion using intraoral images in orthodontics. The...

Role of WNK4 and FKBP5 in asthma: Insights from bioinformatics analysis, machine learning, and preliminary validation.

BACKGROUND: The complex and heterogeneous molecular mechanisms of asthma are currently unclear, and treatment outcomes are dismal in some patients. Th...

Automated landmark detection on lateral photographs using artificial intelligence: diagnostic accuracy compared to expert annotations.

PURPOSE: Lateral photographs are routinely evaluated as part of orthodontic diagnostics and treatment planning. Anthropometric measurements based on s...

Exploration Novel Therapeutic Targets for Periodontitis via Stress Granules Biomarkers.

BACKGROUND: Periodontitis (PD) is associated with stress granules (SGs), which are involved in cellular stress responses. Identifying biomarkers relat...

Fully automatic AI diagnosis of alveolar bone defect in anterior teeth on CBCT images.

OBJECTIVE: To develop a fully automatic artificial intelligence (AI) system for diagnosing alveolar bone defects (ABDs) in anterior teeth on cone-beam...

Bi-level alignment with super-resolution head for unsupervised cephalometric landmark localization.

Objective. Cephalometric alandmark localization is of great clinical significance in diagnosing and treating patients with dental-maxillofacial deform...

SERPINA1 as a Shared Biomarker in Periodontitis and Oral Squamous Cell Carcinoma.

OBJECTIVE: Periodontitis (PD) and oral squamous cell carcinoma (OSCC) frequently co‑occur in clinical populations. However, shared molecular determina...

Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

BACKGROUND: Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect cranio...

Multi-task non-contact ballistocardiogram-based vital signs monitoring in acupuncture.

The study introduces an innovative approach for efficient vital signs monitoring in acupuncture by combining multi-channel ballistocardiogram (BCG) si...

Towards automated model analysis: A multiview AI segmentation of 3D dental scans.

OBJECTIVE: Precise identification of tooth and gingival boundaries in digital models is essential for effective orthodontic diagnosis, treatment plann...

Oral-Rinse-Sourced Microbiota in Oral Health and Diseases in a Representative US Adult Population: Implications for Diagnostics.

AIMS: To investigate the associations between oral-rinse microbiota and distinct oral conditions, and further evaluate its potential ability to distin...

A hybrid MMBERT framework for classifying periodontal bone loss: Integrating visual and textual information.

INTRODUCTION: Periodontitis, a chronic inflammatory disease, causes bone loss and tooth instability. Early diagnosis, aided by clinical and radiograph...

An artificial intelligence-assisted automated quantitative evaluation system for condylar morphological changes of temporomandibular joint.

OBJECTIVES: To develop an artificial intelligence (AI)-assisted system for the automated quantitative evaluation of temporomandibular joint (TMJ) cond...

Multi-Architecture deep learning for CBCT segmentation of dental hard tissues and pulp in mixed dentition.

OBJECTIVE: To develop and evaluate deep learning-based 3D models (CNN, Transformer, and Mamba architectures) for automated segmentation of pulp, prima...

Investigating the mechanism of berberine in inhibiting periodontitis progression using machine learning, molecular docking, and experimental validation.

BACKGROUND: Periodontitis is a chronic inflammatory disease characterized by the destruction of periodontal tissues. Berberine (BBR), a natural alkalo...

AI-driven gingival segmentation on CBCT: Validation using delineation by intraoral scanning and CBCT-based cotton roll separation.

OBJECTIVES: To validate a novel AI approach for automated gingival segmentation directly from cone beam computed tomography (CBCT) scans acquired with...