Digital Systems in Oral Pathology

The introduction of digital images to pathology has transformed this historical discipline into what is currently known as digital pathology (DP). Digital images seem to be well-suitable for computational pathology, which may be used for both complex machine learning, applications, and basic measurement and count. Most intriguingly, images can now be analyzed by machine learning for qualities other than traditional histopathological assessment (artificial intelligence), such as directly connecting images to clinical data including prognosis. Computational pathology, also known as “digital pathology 2.0,” using advanced image analysis algorithms to extract meaningful information from digital pathology images consist a new era. Using algorithms as part of AI it can be used to identify patterns and features that may not be visible to the human eye, which can aid in the diagnosis of diseases. Oral Pathology is an important part of diagnostic procedure for the oral diseases. The severity and pluralism of oral lesions ranging from non-pathologic alterations of oral mucosa to precancerous-cancerous lesions may lead to clinical diagnostic pitfalls. Consequently, digital AI progression of methodology in oral pathology would be a critical step to early and effective diagnosis and prognosis.