Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review
1 Department of Dental Public Health, Faculty of Dental Medicine, Airlangga University, Jl. Prof. Dr. Moestopo No. 47 Surabaya, East Java, Indonesia 60132, Surabaya, Indonesia.
2 Faculty of Dental Medicine, Universitas Airlangga, Jl. Prof. Dr. Moestopo No. 47 Surabaya, East Java, Indonesia 60132, Surabaya, Indonesia.
3 Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jl. Prof. Dr. Moestopo No. 47 Surabaya, East Java, Indonesia 60132, Surabaya, Indonesia.
4 Department of Orthodontics, Faculty of Dental Medicine, Universitas Airlangga Jl. Prof. Dr. Moestopo No. 47 Surabaya, East Java, Indonesia 60132, Surabaya, Indonesia.
Review Article
World Journal of Advanced Research and Reviews, 2022, 16(03), 054-059
Article DOI: 10.30574/wjarr.2022.16.3.1293
Publication history:
Received on 17 October 2022; revised on 28 November 2022; accepted on 30 November 2022
Abstract:
Background: Oral squamous cell carcinoma (OSCC) is defined as an oral malignancy with worldwide prevalence of 90%. In 2018, the number of cases observed is 354.864 with 177.384 deaths globally. Early diagnosis for determining OSCC stage due to histopathological examination is required to sustain prognosis and minimize mortality. Determining the stage is mostly done manually and highly dependent on skill and experiences of the pathologist thus having a high tendency of misdiagnosis. Artificial intelligence (AI) is a technology that modifies machines with human-like intelligence thus making them able to solve the tasks. Utilization of AI in analyzing histopathological samples is known to give such a precision analysis then diagnosing the OSCC stage accurately
Purpose: This study describes utilization of AI-assisted histopathological detection in determining OSCC staging.
Review: Developmental process of OSCC begins with gene damage causing disruption of cell regulation, manifesting in impaired differentiation and proliferation of keratinocytes in the epithelium which is characterized by keratin pearl formation. AI-assisted histopathological detection is able to identify the percentage of keratinization and keratin pearls in histopathological images by convolutional neural network (CNN). CNN is a deep learning architecture specifically designed to recognize two-dimensional visual patterns with minimal preprocessing. CNN works by analyzing input in the form of visual images from histopathological images and producing output as keratinization percentage in related samples then being used to determine the staging of OSCC.
Conclusion: AI-assisted histopathological detection may potential to be used in determining OSCC staging.
Keywords:
Artificial intelligence; Non-communicable disease; Oral squamous cell carcinoma; Dentistry; Medicine
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0