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An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research

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Abstract

Introduction

Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis.

Materials and Methods

We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded.

Results

A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations.

Conclusion

ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.

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Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Literature Review Search Strategy: Mary Ann Williams, MSLS. Research & Education Librarian, Health Sciences & Human Services Library, University of Maryland, Baltimore, USA

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This study was not supported by any funding.

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Author notes

  1. Shahd A. Alajaji and Zaid H. Khoury have contributed equally to this work.

Authors and Affiliations

  1. Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, 650 W. Baltimore Street, 7 Floor, Baltimore, MD, 21201, USA

    Shahd A. Alajaji & Ahmed S. Sultan

  2. Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia

    Shahd A. Alajaji

  3. Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA

    Shahd A. Alajaji & Ahmed S. Sultan

  4. Department of Oral Diagnostic Sciences and Research, Meharry Medical College School of Dentistry, Nashville, TN, USA

    Zaid H. Khoury

  5. Oral Medicine and Pathology Department, School of Dentistry, University of Queensland, Herston, QLD, Australia

    Maryam Jessri

  6. Oral Medicine Department, Metro North Hospital and Health Services, Queensland Health, Brisbane, QLD, Australia

    Maryam Jessri

  7. Department of Otolaryngology, Head & Neck Surgery, The Johns Hopkins University, Baltimore, MD, USA

    James J. Sciubba

  8. University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA

    Ahmed S. Sultan

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S.A.A. and Z.K. wrote the main manuscript text. S.A.A. prepared the figures and tables. All authors reviewed the manuscript.

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Correspondence to Ahmed S. Sultan.

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Alajaji, S.A., Khoury, Z.H., Jessri, M. et al. An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research. Head and Neck Pathol 18, 38 (2024). https://doi.org/10.1007/s12105-024-01643-4

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