Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta‐Analysis
Olga Di Fede, Gaetano La Mantia, Marco Parola, Laura Maniscalco, Domenica Matranga, Pietro Tozzo, Giuseppina Campisi, Mario G. C. A. Cimino
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).