Research progress on the screening technology of cervical cancer
Lu Jingquan, Bu Pataimu·Yusupu, Qin Feng, et al
1. Department of Gynecology, Wuhan Third Hospital, Wuhan, Hubei 430074, China; 2. Department of Gynecology, the Second People's Hospital of Kashgar Prefecture, Xinjiang Uygur Autonomous Region, Kashgar, Xinjiang 844000, China
Cervical cancer is one of the major diseases threatening the health of women worldwide, especially in developing countries where the incidence rate and mortality remain high. Early diagnosis is crucial for improving prognosis, but traditional screening methods such as cytological tests have limitations such as high false positive rate and low sensitivity. With the development of molecular biology and genomics,
understanding of the pathogenesis of cervical cancer has deepened, providing a theoretical basis for the
development of precise detection methods. This article systematically reviews the application progress of
machine learning and deep learning algorithms in cervical cancer screening. These methods can efficiently
identify abnormal cells and achieve automated analysis, with high sensitivity and specificity. At the same time, the integration of artificial intelligence and big data has further enhanced the intelligence level of screening, and is expected to achieve precise risk assessment and personalized diagnosis, providing a new direction for optimizing cervical cancer prevention and control strategies.
2026, Vol. 14 
