Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis
Authors:
Top Cited Papers
Open Access
- 29 February 2024
- journal article
- review article
- Published by Elsevier in Japanese Dental Science Review
- Vol. 60, 128-136
Abstract
No abstract availableKeywords
This publication has 37 references indexed in Scilit:
- Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring systemClinical Oral Investigations, 2022
- Deep-learning approach for caries detection and segmentation on dental bitewing radiographsOral Radiology, 2021
- STAndard Reporting of CAries Detection and Diagnostic Studies (STARCARDDS)Clinical Oral Investigations, 2021
- Systematic review and meta-analysis of diagnostic studies of proximal surface cariesClinical Oral Investigations, 2021
- Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographsClinical Oral Investigations, 2021
- Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy StudiesJAMA, 2018
- Best clinical practice guidance for management of early caries lesions in children and young adults: an EAPD policy documentEuropean Archives of Paediatric Dentistry, 2016
- Radiographic caries detection: A systematic review and meta-analysisJournal of Dentistry, 2015
- Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviewsJournal of Clinical Epidemiology, 2005
- A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluationsStatistics in Medicine, 2001