Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.

Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.

Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing.

A Dataset of apical periodontitis lesions in panoramic radiographs for deep-learning-based classification and detection.

Deep learning has been studied in recent years to identify periapical lesions- a significant indicator of periapical periodontitis in radiographs. An accurate dataset is essential for constructing an efficient learning model for detecting periapical lesions. In order to achieve this goal, we gathered and created a database of panoramic radiographs containing periapical lesions from the High-quality Dental Treatment Centre, School of Dentistry, Hanoi Medical University, between January 2016 and March 2021.

Diagnostic Value of Fluorescence Methods, Visual Inspection and Photographic Visual Examination in Initial Caries Lesion: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis aimed to investigate the efficacy of fluorescence-based methods, visual inspections, and photographic visual examinations in initial caries detection. A literature search was undertaken in the PubMed and Cochrane databases. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines were followed, and eligible articles published from 1 January 2009 to 30 October 2019 were included if they met the following criteria: they (1) assessed the accuracy of methods of detecting initial tooth caries lesions on occlusal, proximal, or smooth surfaces in both primary and permanent teeth (in clinical); (2) used a reference standard; (3) reported data regarding the sample size, prevalence of initial tooth caries, and accuracy of the methods.