Utilizing AI To Predict and Prevent Cervical Cancer: Integrating Machine Learning with Pap Smear and HPV Screening - Baber, Rubina; Latif, Nayar; Raza, Kainat; Zaman, Uzma; Faiza, Faiza; Hafsa, Hafsa (Journal of Neonatal Surgery, 2025)
Background: Cervical cancer remains a substantial cause of morbidity and mortality globally, particularly in areas where access to regular screening is scanty. Routine Pap smears and HPV tests, useful though they are, face issues with sensitivity, specificity and interobserver variability. Artificial intelligence (AI) has the potential to improve cervical cancer screening by providing more accurate, efficient, and prognostic diagnostics when integrated. Objectives: To assess the performance of machine learning models for cervical cancer prediction using combined Pap smear and HPV screening data. Study design: A Retrospective Study. Place and duration of study: Department of Gynae Gomal Medical College Dera Ismail Khan Pakistan From jan 2022 to dec 2022 Methods: This study conducted in Department of Gynae Gomal Medical College Dera Ismail Khan Pakistan From jan 2022 to dec 2022 150 patients with an average age of 25–65 years old who underwent Pap smear and HPV co-testing. We developed a supervised machine learning model for grading cervical intraepithelial neoplasia (CIN). Variables of interest included age, type of HPV, cytological diagnosis, and histopathology. The accuracy, sensitivity and specificity metrics were used to evaluate model performance. Results: 150 patients (mean age: 39.8 ± 8.6 years), 92 (30.6%) had abnormal Pap smear results and 124 (41.3%) were HPV-positive. Fifty-six cases had high-grade CIN. The AI model had an accuracy of 89.7%, sensitivity of 92.1%, and specificity of 86.4% in predicting high-grade lesions. Genital lesions were statistically significantly associated with HPV status (p = 0.002) and CIN grade (p = 0.015) were found. Conclusion: Machine learning offers great promise, and when integrated with traditional screening approaches, can improve predictive accuracy for cervical cancer and promote early detection and intervention. Such an approach is potentially transformative in altering the health care delivery landscape of cervical cancer screening — especially in scenarios where there is limited availability of specialists — and has the potential to translate into a decreased burden of disease and better patient outcomes.