Dental Health

Development of a deep learning classification model using a codeless platform for orthodontic extraction decision-making: Impact of image type on model performance.

OBJECTIVES: To evaluate the effect of image type on the performance of a deep learning classification model for decision-making in orthodontic treatment planning, and to investigate the clinical applicability of artificial intelligence in determining extraction and non-extraction treatment plans using intraoral photographs and digital model scans. METHODS: Pre-treatment patient data and orthodonti...

Multi-view deep learning for mandibular landmark localization.

OBJECTIVES: Accurate localization of anatomical landmarks on the mandible is crucial for maxillofacial surgery and orthodontic treatment planning. This study aims to develop and validate a novel multi-view deep learning framework to enhance the accuracy and efficiency of landmark localization on CBCT-derived 3D mandibular surface models. METHODS: We propose a multi-view stacked hourglass convoluti...

Chatbot content analysis of patient information on orthodontic tooth extractions.

INTRODUCTION: This study aims to evaluate the quality, accuracy, readability, and understandability of patient information provided by various Artific...

A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software.

OBJECTIVES: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semi-automated tool for identifying craniofac...

Self-reported periodontitis: A multiethnic community-based validation study.

OBJECTIVE: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation effort...

Deep learning-based tooth segmentation for enhanced visualization of dental anomalies and pathologies.

BACKGROUND: This study aimed to developed and validated a deep-learning method for instance-level tooth segmentation in CBCT to enhance visualization ...

Prediction of unerupted canine and premolars width using artificial intelligence compared with Tanaka-Johnston and Moyers methods: A retrospective observational analytical study on Iranian population.

INTRODUCTION: Accurate prediction of unerupted teeth widths significantly contributes to formulating optimal treatment plan during mixed dentition pha...

Machine Learning-Based Transcriptomic Diagnosis of Periodontitis.

BACKGROUND: Periodontitis, a prevalent chronic inflammatory disease, remains a global health challenge with conventional diagnostic methods hindered b...

Corrigendum to 'Decoding Epigenetic Enhancer-Promoter Interactions in Periodontitis via Transformer-GAN: A Deep Learning Framework for Inflammatory Gene Regulation and Biomarker Discovery' [International Dental Journal Volume 75, Issue 6, December 2025, 103879].

Graph-Contrastive Convolutional Neural Network for Extracting and Classifying Peptide-Based Periodontal Immunomodulatory and Anti-Inflammatory Signatures.

INTRODUCTION: Periodontitis is a chronic inflammatory disease that occurs when the body's immune system fails to respond properly to microbial biofilm...

Transcription Accuracy of Automatic Speech Recognition for Orthodontic Clinical Records.

Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language ...

Single-Cell and Machine Learning Analysis Reveal Novel Inflammatory Macrophage Subtypes and Biomarkers in Periodontitis.

BACKGROUND: Periodontitis (PD) is a chronic inflammatory disease marked by immune dysregulation and progressive tissue destruction. Macrophages play a...

Dental Crowding Categorization Network (DCC-Net): Explainable deep learning system for automatic categorization of dental crowding on intraoral photographs.

OBJECTIVE: Since categorization of dental crowding is a crucial parameter in orthodontic diagnosis and tooth-extraction decisions, we aimed to develop...

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 u...

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 ...

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...

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....