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Molar

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Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.

Clinical oral investigations
OBJECTIVES: The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the r...

A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography.

Journal of endodontics
INTRODUCTION: The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular ...

Artificial intelligence-designed single molar dental prostheses: A protocol of prospective experimental study.

PloS one
BACKGROUND: Dental prostheses, which aim to replace missing teeth and to restore patients' appearance and oral functions, should be biomimetic and thus adopt the occlusal morphology and three-dimensional (3D) position of healthy natural teeth. Since ...

Development of an artificial intelligence-based algorithm to classify images acquired with an intraoral scanner of individual molar teeth into three categories.

PloS one
BACKGROUND: Forensic dentistry identifies deceased individuals by comparing postmortem dental charts, oral-cavity pictures and dental X-ray images with antemortem records. However, conventional forensic dentistry methods are time-consuming and thus u...

Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs.

Journal of endodontics
INTRODUCTION: The purpose of this study was to develop and validate a visually explainable deep learning model for the classification of C-shaped canals of the mandibular second molars in dental radiographs.

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...

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.

[A deep learning segmentation model for detecting caries in molar teeth].

Zhonghua yi xue za zhi
This study aimed to build a home use deep learning segmentation model to identify the scope of caries lesions. A total of 494 caries photographs of molars and premolars collected via endoscopy were selected. Subsequently, these photographs were label...

Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies.

Clinical oral investigations
OBJECTIVES: Molar incisor hypomineralization (MIH) is a difficult-to-diagnose developmental disorder of the teeth, mainly in children and adolescents. Due to the young age of the patients, problems typically occur with the diagnosis of MIH. The aim o...

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.

Scientific reports
In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presenc...