Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis

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.

Keywords: artificial intelligence; deep learning; machine learning; radiography; tooth detecting.

Figure 1.

PRISMA flow chart for included studies.

Figure 2.

Risk of bias assessment of included studies based on each domain.

Figure 3.

Individual studies are represented by squares and lines, and 95% CIs are indicated by their 95% squares and lines. Pooled values are shown in diamonds. Statistical heterogeneity is represented by I² and Q by weighted sums of squares differences, respectively.

Figure 4.

(a) ROC curve summary. Each circle represents a different study, whereas the solid square represents the intersection point between sensitivity and specificity. (b) In order to assess publication bias, funnel plots are used.