Abstract
Objectives
To thoroughly review Deep Convolutional Neural Networks for detecting interproximal caries with bitewing radiographs.
Data
Data was collected from studies that utilized Deep Convolutional Neural Networks (DCNN) focused on the analysis of bitewing radiographs taken with intraoral X-ray units.
Sources
A comprehensive literature search was conducted across various scholarly databases including Google Scholar, MDPI, PubMed, ResearchGate, ScienceDirect, and IEEE Xplore, encompassing 2014 to 2024. The risk of bias assessment utilized the current version of the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2).
Study selection
After reviewing 291 articles, 10 studies met the criteria and were analyzed. All 10 studies used bitewing radiographs, focusing on deep learning tasks such as segmentation, classification, and detection. The sample sizes varied widely from 112 to 3,989 participants. Convolutional neural networks (CNNs) were the most commonly used model. According to the QUADAS-2 assessment, only 40% of the studies included in this review were found to have a low risk of bias in the reference standard domain.
Clinical significance
A Deep Convolutional Neural Networks based caries detection system helps in the early identification of caries by analyzing bitewing radiographs and reduces diagnostic errors. By identifying early-stage lesions, patients can undergo minimally invasive treatments instead of more complex procedures, thereby improving patient outcomes in dental care.
Conclusion
This systematic review provides an overview of various studies that utilize deep learning models to identify interproximal caries lesions in bitewing radiographs. It highlights the efficacy of YOLOv8 in detecting interproximal caries from bitewing radiographs compared to other Deep CNN models.
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Fig. 1
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Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request or in PROSPERO. CRD42024578953
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Funding
This study was conducted without the support of external funding.
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Israel Domilin Shyni
Present address: Department of Information Technology, St. Joseph’s College of Engineering, 600119, Chennai, India
Authors and Affiliations
Department of Information Technology, St. Xavier’s Catholic College of Engineering, Nagercoil, India
Soundar Ida Mahizha & Joseph Annrose
Faculty of Dentistry, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
Jeyebalaji Mano Christaine Angelo
Department of Computer Science and Engineering, DMI College of Engineering, Chennai, India
Israel Domilin Shyni
Department of OMFs, Faculty of Dentistry, Sri ramachandra Institute of Higher Education and Research, Chennai, India
G. valanthan veda Giri
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Soundar Ida Mahizha
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Contributions
Soundar Ida Mahizha: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing- original draft, Writing- review & editing. Jeyabalaji Mano Christaine Angelo: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing- original draft, Writing- review & editing; Joseph Annrose: Conceptualization, Project administration, Supervision, Formal analysis, Investigation, Resources, Writing- original draft, Writing- review & editing; Israel Domilin Shyni: Supervision, Formal analysis, Investigation, Resources, Writing- original draft; Valanthan Veda Giri: Formal analysis, Methodology, Validation, Visualization, Writing- original draft, Writing- review & editing.
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Mahizha, S.I., Annrose, J., Mano Christaine Angelo, J. et al. Deep convolutional neural networks for early detection of interproximal caries using bitewing radiographs: A systematic review. Evid Based Dent (2025). https://doi.org/10.1038/s41432-025-01134-7
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Received:07 October 2024
Accepted:17 December 2024
Published:21 March 2025
DOI:https://doi.org/10.1038/s41432-025-01134-7
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