AIMC Topic: Radiography, Panoramic

Clear Filters Showing 111 to 120 of 238 articles

Diagnosing oral and maxillofacial diseases using deep learning.

Scientific reports
The classification and localization of odontogenic lesions from panoramic radiographs is a challenging task due to the positional biases and class imbalances of the lesions. To address these challenges, a novel neural network, DOLNet, is proposed tha...

Artificial intelligence and dental panoramic radiographs: where are we now?

Evidence-based dentistry
DATA SOURCES: Bielefeld Academic Search Engine (BASE), Google Scholar Association for Computing Machinery: Guide to Computing Literature (ACM) and National Library of Medicine: PubMed databases were searched for systematic reviews.

Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising r...

Development of a dental digital data set for research in artificial intelligence: the importance of labeling performed by radiologists.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to present the development of a database (dataset) of panoramic radiographs.

Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.

Scientific reports
Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact...

Risk assessment of inferior alveolar nerve injury after wisdom tooth removal using 3D AI-driven models: A within-patient study.

Journal of dentistry
OBJECTIVE: To compare a three-dimensional (3D) artificial intelligence (AI)- driven model with panoramic radiography (PANO) and cone-beam computed tomography (CBCT) in assessing the risk of inferior alveolar nerve (IAN) injury after mandibular wisdom...

The role of deep learning for periapical lesion detection on panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVE: This work aimed to detect automatically periapical lesion on panoramic radiographs (PRs) using deep learning.

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.

BMC oral health
BACKGROUND: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and ...

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs.

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs.

Journal of forensic sciences
The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study inv...