AIMC Topic: Malocclusion
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Automated classification of skeletal malocclusion in German orthodontic patients.
Clinical oral investigations
Aug 5, 2025
OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differen...
Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns.
BMC oral health
Jul 26, 2025
BACKGROUND: Craniofacial phenotyping is essential for individualized orthodontic diagnosis and treatment planning. Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine l...
Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.
BMC oral health
Apr 8, 2025
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...
Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.
Head & face medicine
Mar 28, 2025
BACKGROUND: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of inter...
Classification of skeletal discrepancies by machine learning based on three-dimensional facial scans.
International journal of oral and maxillofacial surgery
Mar 17, 2025
The aim of this study was to use machine learning (ML) to classify sagittal and vertical skeletal discrepancies in three-dimensional (3D) facial scans, as well as to evaluate shape variability. 3D facial scans from 435 pre-orthodontic patients were s...
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...
Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.
Biomedical engineering online
Feb 4, 2025
BACKGROUND: Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodon...
A Fully Integrated Orthodontic Aligner With Force Sensing Ability for Machine Learning-Assisted Diagnosis.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Nov 19, 2024
Currently, the diagnosis of malocclusion is a highly demanding process involving complicated examinations of the dental occlusion, which increases the demand for innovative tools for occlusal data monitoring. Nevertheless, continuous wireless monitor...
Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review.
Journal of dentistry
Nov 4, 2024
OBJECTIVES: To provide an overview of artificial intelligence (AI) applications in orthodontic diagnosis and treatment planning, and to evaluate whether AI improves accuracy, reliability, and time efficiency compared to expert-based manual approaches...
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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