AIMC Topic: Malocclusion

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A cross-temporal multimodal fusion system based on deep learning for orthodontic monitoring.

Computers in biology and medicine
INTRODUCTION: In the treatment of malocclusion, continuous monitoring of the three-dimensional relationship between dental roots and the surrounding alveolar bone is essential for preventing complications from orthodontic procedures. Cone-beam comput...

Designing an artificial intelligence system for dental occlusion classification using intraoral photographs: A comparative analysis between artificial intelligence-based and clinical diagnoses.

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: This study aimed to design an artificial intelligence (AI) system for dental occlusion classification using intraoral photographs. Moreover, the performance of this system was compared with that of an expert clinician.

Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study.

Medicina (Kaunas, Lithuania)
: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as ...

Orthodontic Aligners: Current Perspectives for the Modern Orthodontic Office.

Medicina (Kaunas, Lithuania)
Orthodontic aligners are changing the practice of orthodontics. This system of orthodontic appliances is becoming the mainstay appliance of choice for orthodontic offices in many countries. Patient preferences and lifestyle needs have made this appli...

Evaluation of AI Model for Cephalometric Landmark Classification (TG Dental).

Journal of medical systems
The accuracy of cephalometric landmark identification for malocclusion classification is essential for diagnosis and treatment planning. Identifying these landmarks is often complex and time-consuming for orthodontists. An AI model for classification...

Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation o...

Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs.

BMC oral health
BACKGROUND: It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into norm...

Artificial intelligence system for automated landmark localization and analysis of cephalometry.

Dento maxillo facial radiology
OBJECTIVES: Cephalometric analysis is essential for diagnosis, treatment planning and outcome assessment of orthodontics and orthognathic surgery. Utilizing artificial intelligence (AI) to achieve automated landmark localization has proved feasible a...

Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software.

Dental press journal of orthodontics
OBJECTIVE: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagi...