3.8 Review

Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review

Journal

IMAGING SCIENCE IN DENTISTRY
Volume 51, Issue 3, Pages 237-242

Publisher

KOREAN ACAD ORAL & MAXILLOFACIAL RADIOLOGY
DOI: 10.5624/isd.20210074

Keywords

Deep Learning; Neural Network Models; Dental Caries; Radiography; Dental

Funding

  1. Padjadjaran University [1733/UN6.3.1/LT/2020]
  2. Ministry of Higher Education of Malaysia [FRGS 15-251-0492]

Ask authors/readers for more resources

This study analyzed and reviewed deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Findings suggest that these networks have the potential to enhance precision in detecting and diagnosing carious lesions, ultimately improving patient outcomes.
Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available