AIMC Topic: Radiography, Dental, Digital
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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss.
Clinical oral investigations
Mar 19, 2025
OBJECTIVE: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL).
Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.
Oral radiology
Dec 9, 2024
OBJECTIVES: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards digital dentistry. The performance of deep learning models heavily relies on the quality and divers...
Automated Identification of Dental Implants: A New, Fast and Accurate Artificial Intelligence System.
The European journal of prosthodontics and restorative dentistry
May 31, 2024
INTRODUCTION: Prosthetic complications that occur to some implant prosthetics may require removal of the prosthesis for replacement or repair. Therefore, the presence of a technique to identify the type of dental implant is mandatory to provide the s...
Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise.
Journal of the American Dental Association (1939)
Mar 23, 2024
BACKGROUND: Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide inf...
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
Dec 14, 2023
OBJECTIVE: The aim of this study was to present the development of a database (dataset) of panoramic radiographs.
Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques.
Oral surgery, oral medicine, oral pathology and oral radiology
Jun 5, 2022
OBJECTIVE: The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs.
The missing link in image quality assessment in digital dental radiography.
Oral radiology
Jul 13, 2019
Digital radiography is gaining popularity among general dental practitioners. It includes digital intraoral radiography, digital panoramic radiography, digital cephalography, and cone-beam computed tomography. In this study, we focused on the methods...
Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: Periodontitis is a serious periodontal infection that damages the soft tissues and bone around teeth and is linked to systemic conditions. Accurate diagnosis and staging, complemented by radiographic evaluation, are vital. This systematic...
Microbiological contamination in digital radiography: evaluation at the radiology clinic of an educational institution.
Acta odontologica latinoamericana : AOL
Dec 1, 2016
The aim of this study was to evaluate the contamination rate of intra and extraoral digital X ray equipment in a dental radiology clinic at a public educational institution. Samples were collected on three different days, at two times in the day: in ...
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