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.

Olga Di Fede, La Mantia Gaetano, Parola Marco, Maniscalco Laura, Matranga Domenica, Tozzo Pietro, et al. (2024). Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta‐Analysis [10.1111/odi.15188].

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

Olga Di Fede;La Mantia Gaetano;Maniscalco Laura;Matranga Domenica;Tozzo Pietro;Campisi Giuseppina
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2024-01-01

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.
2024
Olga Di Fede, La Mantia Gaetano, Parola Marco, Maniscalco Laura, Matranga Domenica, Tozzo Pietro, et al. (2024). Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta‐Analysis [10.1111/odi.15188].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/664520
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