AIMC Topic: Malocclusion

Clear Filters Showing 1 to 10 of 35 articles

Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.

PloS one
BACKGROUND: Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an ea...

Pilot case control evaluation of artificial intelligence assisted orthodontic monitoring and pediatric patient perception.

Scientific reports
Artificial Intelligence (AI) has become a key tool in the modernization of the healthcare industry, aiding dentists in performing their work more efficiently and effectively. The aim of this study was to evaluate orthodontic monitoring and patient pe...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.

BMC oral health
BACKGROUND: Generative artificial intelligence (AI) models have shown potential for addressing text-based dental enquiries and answering exam questions. However, their role in diagnosis and treatment planning has not been thoroughly investigated. Thi...

Automated classification of skeletal malocclusion in German orthodontic patients.

Clinical oral investigations
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
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
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
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
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
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...