AIMC Topic: Radiography, Dental
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Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?
Oral radiology
Oct 9, 2024
OBJECTIVE: In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. T...
Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Aug 23, 2024
The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in Novem...
Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application-A randomized clinical trial.
European journal of dental education : official journal of the Association for Dental Education in Europe
Jul 31, 2024
INTRODUCTION: Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education.
RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study.
Journal of imaging informatics in medicine
Jul 24, 2024
Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale ...
Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.
International dental journal
Jun 8, 2024
Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was...
Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset.
Scientific reports
Jun 1, 2024
Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an ...
Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning.
Journal of endodontics
May 29, 2024
INTRODUCTION: Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal mo...
Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs.
Journal of dentistry
May 29, 2024
OBJECTIVES: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs.
Deep learning for determining the difficulty of endodontic treatment: a pilot study.
BMC oral health
May 17, 2024
BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.
Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions.
Journal of dentistry
May 10, 2024
OBJECTIVE: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic imag...
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