Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review

Background: Oral squamous cell carcinoma (OSCC) is defined as an oral malignancy with worldwide prevalence of 90%. In 2018, the number cases observed 354.864 177.384 deaths globally. Early diagnosis for determining OSCC stage due to histopathological examination required sustain prognosis and minimize mortality. Determining mostly done manually highly dependent on skill experiences pathologist thus having a high tendency misdiagnosis. Artificial intelligence (AI) technology that modifies machines human-like making them able solve tasks. Utilization AI in analyzing samples known give such precision analysis then diagnosing accurately Purpose: This study describes utilization AI-assisted detection staging. Review: Developmental process begins gene damage causing disruption regulation, manifesting impaired differentiation proliferation keratinocytes epithelium which characterized by keratin pearl formation. identify percentage keratinization pearls images convolutional neural network (CNN). CNN deep learning architecture specifically designed recognize two-dimensional visual patterns minimal preprocessing. works input form from producing output related being used determine staging OSCC. Conclusion: may potential be