AIMC Topic: Radiography, Bitewing
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Evaluation of Caries Detection on Bitewing Radiographs: A Comparative Analysis of the Improved Deep Learning Model and Dentist Performance.
Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
Apr 7, 2025
OBJECTIVES: The application of deep learning techniques for detecting caries in bitewing radiographs has gained significant attention in recent years. However, the comparative performance of various modern deep learning models and strategies to enhan...
ARTIFICIAL INTELLIGENCE DEMONSTRATES POTENTIAL IN DETECTING CARIES ON BITEWING RADIOGRAPHS, BUT FURTHER HIGH-QUALITY STUDIES ARE REQUIRED.
The journal of evidence-based dental practice
Jan 4, 2025
ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: A systematic review and meta-analysis. Ammar, N. & Kühnisch, J. Japanese Dental Science Review, 60(2024): 1...
Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis.
Journal of dentistry
Oct 11, 2024
OBJECTIVES: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs.
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.
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.
AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach.
Journal of imaging informatics in medicine
May 14, 2024
Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of s...
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...
Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.
Journal of dentistry
Mar 30, 2024
OBJECTIVES: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the car...
AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study.
BMC oral health
Mar 18, 2024
BACKGROUND: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligenc...
Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 20, 2024
OBJECTIVE: The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in th...
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