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AI-supported screening strategy could improve cervical cancer detection in low-resource settings

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AI-supported screening strategy could improve cervical cancer detection in low-resource settings

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Combining HPV testing with artificial intelligence–based image analysis has shown efficacy in detecting precancerous lesions and guiding treatment decisions, according to a study led by the Barcelona Institute for Global Health (ISGlobal). The research, conducted within the HPV-Automated Visual Evaluation (PAVE) Consortium across nine countries, has been published in The Lancet Obstetrics, Gynaecology & Women's Health.

Despite global efforts such as the WHO cervical cancer elimination initiative, screening coverage remains below 15% in many low- and middle-income countries. Existing approaches, including visual inspection with acetic acid, often lack accuracy, while access to high-quality HPV testing and follow-up services remains uneven. To address these gaps, the PAVE study evaluated a strategy that integrates self-collected HPV testing with extended genotyping and automated visual evaluation (AVE) of cervical images. This approach is designed to be scalable and adaptable to settings with limited infrastructure, while maintaining high diagnostic performance.

The study enrolled more than 50,000 women aged 25 to 49 years between 2022 and 2025 in Brazil, Cambodia, the Dominican Republic, El Salvador, Eswatini, Honduras, Malawi, Nigeria and Tanzania. Participants provided self-collected samples for HPV testing capable of identifying 13 carcinogenic virus types. Women who tested positive underwent cervical imaging using a portable colposcope, followed by biopsy to confirm diagnosis. The images were analyzed by a deep-learning automated visual evaluation (AVE) algorithm, which classified findings as severe, indeterminate or normal.


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