Cervical cancer remains a leading cause of morbidity and mortality among women worldwide, particularly in regions with limited access to advanced medical care. Accurate and timely diagnosis of precancerous changes in the cervix is critical for effective prevention and treatment. This study introduces a deep learning algorithm for colposcopic analysis of the transformation zone of the uterine cervix. Intel & MobileODT Cervical Cancer Screening competition provided a comprehensive dataset designed to advance the application of artificial intelligence (AI) in classifying transformation zones (TZ) of the cervix, a key site where precancerous changes develop due to Human Papillomavirus (HPV) infection. This study highlights the significance of TZ classification for targeted biopsy during colposcopy, a gold-standard diagnostic method. However, challenges such as clinician’s subjectivity and interobserver variability, false negatives and positives interpretations limited accessibility, and resource intensity have spurred the integration of AI into colposcopic evaluations. The dataset comprises diverse cervical images, categorized into three types of TZs, enabling the development of AI models to distinguish between these categories. By leveraging deep learning algorithms, AI has demonstrated potential in enhancing the sensitivity and specificity of colposcopic findings while mitigating subjectivity and observer dependency. This abstract outlines the anatomical basis of cervical pathology, the critical role of colposcopy in diagnosing transformation zone abnormalities, and the transformative potential of AI in improving cervical cancer screening processes. The integration of AI-assisted tools could significantly improve diagnostic accuracy, reduce invasive procedures, and enhance access to cervical cancer prevention measures, particularly in underserved regions.

AI, cancer screening, cervical cancer, precancerous changes