AI Medical Compendium Topic

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Tooth

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Benchmarking Deep Learning Models for Tooth Structure Segmentation.

Journal of dental research
A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Benchmarking (i.e., the systematic comparison of state-of-the ar...

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...

Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children.

Dento maxillo facial radiology
OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology.

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...

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...

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...

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...

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.

Dentronics: tooth cleaning with a tactile collaborative robot - an in vitro proof of concept.

International journal of computerized dentistry
AIM: The aim of the present study was to compare the performance of a tactile collaborative robot programmed by a dental professional (DP) with that of a DP in the removal of surrogate plaque in vitro.