Deep Learning for Tooth Identification and Numbering on Dental Radiography: a Systematic Review and Meta-analysis

Overview

Journal Dentomaxillofac Radiol

Publisher Oxford University Press

Date 2024 Jan 5

PMID 38183164

Authors

Soroush Sadr

Rata Rokhshad

Yasaman Daghighi

Mohsen Golkar

Fateme Tolooie Kheybari

Fatemeh Gorjinejad

Atousa Mataji Kojori

Parisa Rahimirad

Parnian Shobeiri

Mina Mahdian

Hossein Mohammad-Rahimi

Affiliations

Soon will be listed here.

Abstract

Objectives: Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification.

Methods: An electronic search was performed through October 2023 on PubMed, Scopus, Cochrane, Google Scholar, IEEE, arXiv, and medRxiv. Studies that used deep learning models with segmentation, object detection, or classification tasks for teeth identification and numbering of human dental radiographs were included. For risk of bias assessment, included studies were critically analysed using quality assessment of diagnostic accuracy studies (QUADAS-2). To generate plots for meta-analysis, MetaDiSc and STATA 17 (StataCorp LP, College Station, TX, USA) were used. Pooled outcome diagnostic odds ratios (DORs) were determined through calculation.

Results: The initial search yielded 1618 studies, of which 29 were eligible based on the inclusion criteria. Five studies were found to have low bias across all domains of the QUADAS-2 tool. Deep learning has been reported to have an accuracy range of 81.8%-99% in tooth identification and numbering and a precision range of 84.5%-99.94%. Furthermore, sensitivity was reported as 82.7%-98% and F1-scores ranged from 87% to 98%. Sensitivity was 75.5%-98% and specificity was 79.9%-99%. Only 6 studies found the deep learning model to be less than 90% accurate. The average DOR of the pooled data set was 1612, the sensitivity was 89%, the specificity was 99%, and the area under the curve was 96%.

Conclusion: Deep learning models successfully can detect, identify, and number teeth on dental radiographs. Deep learning-powered tooth numbering systems can enhance complex automated processes, such as accurately reporting which teeth have caries, thus aiding clinicians in making informed decisions during clinical practice.

Citing Articles

Evaluation of the Performance of a YOLOv10-Based Deep Learning Model for Tooth Detection and Numbering on Panoramic Radiographs of Patients in the Mixed Dentition Period.

Peker R, Kurtoglu C Diagnostics (Basel). 2025; 15(4).

PMID: 40002557 PMC: 11854638. DOI: 10.3390/diagnostics15040405.

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

Beser B, Reis T, Berber M, Topaloglu E, Gungor E, Kilic M BMC Med Imaging. 2024; 24(1):172.

PMID: 38992601 PMC: 11238494. DOI: 10.1186/s12880-024-01338-w.

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