AIMC Topic: Tooth

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Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to assess the performance of the deep learning (DL) model for automated tooth numbering in panoramic radiographs.

Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping.

BMC oral health
BACKGROUND: Sexual dimorphism is obvious not only in the overall architecture of human body, but also in intraoral details. Many studies have found a correlation between gender and morphometric features of teeth, such as mesio-distal diameter, buccal...

Automatic dental biofilm detection based on deep learning.

Journal of clinical periodontology
AIM: To estimate the automated biofilm detection capacity of the U-Net neural network on tooth images.

The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review.

Acta odontologica Scandinavica
OBJECTIVES: To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.

Emergence angle: Comprehensive analysis and machine learning prediction for clinical application.

Journal of prosthodontic research
PURPOSE: To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable...

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography.

Scientific reports
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the perfor...

Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net.

International journal of oral and maxillofacial surgery
The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multip...

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

IEEE transactions on medical imaging
Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, hig...

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

Sensors (Basel, Switzerland)
The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Olde...

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs.

Computers in biology and medicine
Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's asse...