AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Dentition, Mixed

Showing 1 to 8 of 8 articles

Clear Filters

Estimating the size of unerupted teeth: Moyers vs deep learning.

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 a deep learning (DL) system for estimating the sum of the mesiodistal widths (MDWs) of unerupted mandibular canines and premolars in the mixed dentition period and to clarify its performance by comparing DL es...

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

International journal of paediatric dentistry
BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net.

International journal of paediatric dentistry
AIM: The purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no-new-Ne...

Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consist...

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

BMC medical imaging
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and...

Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning.

Journal of dentistry
OBJECTIVE: To establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images.

A deep-learning system for diagnosing ectopic eruption.

Journal of dentistry
OBJECTIVES: To construct a diagnostic model for mixed dentition using a multistage deep-learning network to predict potential ectopic eruption in permanent teeth by integrating dentition segmentation into the process of automatic classification of de...

A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.

BMC oral health
BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photograp...