Natural Language Processing Techniques in Diagnostic Digital Cytopathology for Cervical Intraepithelial Neoplasia Detection

Cervical cancer is the third most common cancer in women diagnosed, according to figures from developing nations. One of the well-known risk factors for cervical cancer, HPV 16 or HPV 18, is responsible for more than 70% of new instances of the disease. The human papillomavirus (HPV) changed cervical cancer screening, and researchers started seeking for alternatives to conventional cytological exams that would be helpful in underdeveloped areas. A key step in the pathophysiology of cervical cancer is natural language processing (NLP), which is essential for retrieving structured data from electronic health records (EHRs). It is essential to improving healthcare. In the past ten years, the area of pathology has made a number of advancements as a result of the use of NLP to pathology reports. This work focuses on the integration of natural language processing into the creation of digital cytopathology systems for the identification of cervical intraepithelial neoplasia.