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Meta-Analysis
. 2025 Apr;31(4):1054-1064.
doi: 10.1111/odi.15188. Epub 2024 Nov 3.

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis

Olga Di Fede  1 , Gaetano La Mantia  1   2   3 , Marco Parola  4 , Laura Maniscalco  5 , Domenica Matranga  5 , Pietro Tozzo  6 , Giuseppina Campisi  2   7 , Mario G C A Cimino  4
Affiliations
Meta-Analysis

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis

Olga Di Fede et al. Oral Dis. 2025 Apr.

Abstract

Objective: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.

Materials and methods: A scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.

Results: Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.

Conclusions: The results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.

Trial registration: Open Science Framework (https://osf.io/4n8sm).

Keywords: automatic detection; convolutional neural networks; deep learning; meta‐analysis; oral cancer; oral lesion classification.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA flow diagram for scoping reviews.
See this image and copyright information in PMC

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