Cervical cancer is a severe threat to women’s health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screening 70% of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical cancer.
As part of the efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategies. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical images.
The study highlights how AI can automate the segmentation and classification of cytology images, which is vital for early diagnosis. Additionally, it explores AI’s potential to enhance colposcopy, a procedure traditionally hampered by subjective interpretation and reliance on highly skilled professionals. These models, powered by machine learning, offer a personalized approach to screening, reducing unnecessary referrals and allowing for better risk stratification.
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