Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Parola, Marco; Mantia, Gaetano La; Galatolo, Federico; Cimino, Mario G.C.A.; Campisi, Giuseppina; Di Fede, Olga

doi:10.1109/ssci52147.2023.10371865

2023 IEEE Symposium Series on Computational Intelligence (SSCI) 2023

DOI: 10.1109/ssci52147.2023.10371865

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Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Marco Parola,

Gaetano La Mantia,

Federico Galatolo

et al.

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2024

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Cited by 2 publications

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

Di Fede,

La Mantia,

Parola

et al. 2024

Oral Diseases

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ObjectiveOral 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 MethodsA 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.ResultsFourteen 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.ConclusionsThe results of meta‐analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.Trial RegistrationOpen Science Framework (https://osf.io/4n8sm)

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

Di Fede,

La Mantia,

Parola

et al. 2024

Oral Diseases

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Detection of Elementary White Mucosal Lesions by an AI System: A Pilot Study

La Mantia,

Kiswarday,

Pizzo

et al. 2024

Oral

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Aim: Accurately identifying primary lesions in oral medicine, particularly elementary white lesions, is a significant challenge, especially for trainee dentists. This study aimed to develop and evaluate a deep learning (DL) model for the detection and classification of elementary white mucosal lesions (EWMLs) using clinical images. Materials and Methods: A dataset was created by collecting photographs of various oral lesions, including oral leukoplakia, OLP plaque-like and reticular forms, OLL, oral candidiasis, and hyperkeratotic lesions from the Unit of Oral Medicine. The SentiSight.AI (Neurotechnology Co.®, Vilnius, Lithuania) AI platform was used for image labeling and model training. The dataset comprised 221 photos, divided into training (n = 179) and validation (n = 42) sets. Results: The model achieved an overall precision of 77.2%, sensitivity of 76.0%, F1 score of 74.4%, and mAP of 82.3%. Specific classes, such as condyloma and papilloma, demonstrated high performance, while others like leucoplakia showed room for improvement. Conclusions: The DL model showed promising results in detecting and classifying EWMLs, with significant potential for educational tools and clinical applications. Expanding the dataset and incorporating diverse image sources are essential for improving model accuracy and generalizability.

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Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Cited by 2 publications

References 16 publications

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

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

Detection of Elementary White Mucosal Lesions by an AI System: A Pilot Study

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