Dental Health

Machine learning in orthodontics: Transforming Invisalign treatment planning through precision, interpretability, and ethical practices.

BACKGROUND: The integration of artificial intelligence (AI) in healthcare, particularly in orthodontics, is evolving rapidly. This study leverages a unique dataset from Thai patients undergoing Invisalign treatment to explore the synergy between AI and clinical orthodontics. OBJECTIVE: This research aims to augment the predictability and personalization of Invisalign treatment plans via advanced m...

Are answers obtained from artificial intelligence models for information purposes repeatable?

INTRODUCTION: The objective of this study was to assess the repeatability of orthodontic responses generated by multiple large language models across repeated time points. METHODS: This experimental study assessed the answers provided by ChatGPT-3.5, ChatGPT-4.0, Gemini, and Gemini-Advanced to 40 frequently asked orthodontic questions. Each model was prompted with the same questions at three time ...

Comparative evaluation of ChatGPT, Gemini, and Grok in clinical decision-making and general knowledge assessment for impacted maxillary canines.

OBJECTIVE: This study aimed to compare extraction versus orthodontic eruption decisions for impacted maxillary canines made by three artificial intell...

Comparative Evaluation of ChatGPT-4o and Grok-3 on Cleft Lip and Palate and Presurgical Infant Orthopedics: A Multidisciplinary Assessment by Orthodontists, Pediatricians, and Plastic Surgeons.

Objective: This study aimed to evaluate and compare the accuracy, clarity, and clinical applicability of 2 state-of-the-art large language models (LLM...

Early prediction of orthodontic gingival enlargement using S100A4: a biomarker-based risk stratification model.

Orthodontic-induced gingival enlargement (OIGE) affects approximately 15-30% of patients undergoing orthodontic treatment and remains largely unpredic...

SERS spectrum of saliva combined with machine learning algorithm for early diagnosis and monitoring of periodontal disease.

To achieve non-invasive early diagnosis and severity monitoring of periodontal disease, this study employed silver nanoparticles as a surface-enhanced...

Deep Learning Photo Processing for Periodontitis Screening.

Late detection of periodontitis has significant health implications. Screening via oral images may serve as an accessible nonclinical method. This stu...

Multimodal deep learning for cephalometric landmark detection and treatment prediction.

In orthodontics and maxillofacial surgery, accurate cephalometric analysis and treatment outcome prediction are critical for clinical decision-making....

Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment.

This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learnin...

Machine learning molecular dynamics reveals atomistic mechanisms of ion beam damage in calcium fluoride.

Calcium fluoride (CaF) crystals are regarded as ideal materials for optical components due to their excellent ultraviolet (UV) transmittance and optic...

Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.

Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limit...

Comment on "Deep Learning for Staging Periodontitis Using Panoramic Radiographs".

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.

Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision med...

Exploring the complex interplay between oral infection, periodontitis, and robust microRNA induction, including multiple known oncogenic miRNAs.

() is an oral commensal bacterium that can become pathogenic and is associated with periodontitis, adverse pregnancy outcomes, and colorectal cancer ...

Building and Evaluating an Orthodontic Natural Language Processing Model for Automated Clinical Note Information Extraction.

INTRODUCTION: Malocclusion presents functional and aesthetic challenges, necessitating accurate diagnosis and treatment. However, variability in ortho...

Enzymatic carbon-fluorine bond cleavage by human gut microbes.

Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many mole...

Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis.

BACKGROUND: Environmental pollutants, particularly from air pollution and tobacco smoke, have emerged as significant risk factors. Benzopyrene (BaP), ...

Comparative analysis of AI chatbot (ChatGPT-4.0 and Microsoft Copilot) and expert responses to common orthodontic questions: patient and orthodontist evaluations.

OBJECTIVE: The aim of this study was to evaluate the adequacy of responses provided by experts and artificial intelligence-based chatbots (ChatGPT-4.0...

The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review.

BACKGROUND: Generative AI technologies offer significant opportunities to enhance orthodontic education by improving knowledge retention, clinical dec...

Automated periodontal assessment in orthodontic patients: a dual CNN framework.

OBJECTIVE: The aim of this study was to develop convolutional neural network (CNN)-based systems to diagnose calculus, plaque, gingival hyperplasia an...