AI-supported screening strategy could improve cervical cancer detection in low-resource settings
· Medical Xpressby Barcelona Institute for Global Health
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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.
Combining HPV testing and AI to refine risk assessment
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.
"Among nearly 49,000 women with valid results, more than one in four tested positive for HPV, and most attended follow-up assessment," explains Silvia de Sanjosé, associate researcher at ISGlobal and first author of the study. "We identified over 500 cases of severe precancerous lesions—known as CIN3+, the most advanced stage before cervical cancer—with higher prevalence among women living with HIV."
The risk of severe lesions increased according to both HPV type and the severity of abnormalities detected by the AI system. Infections with HPV16 (the most carcinogenic type) showed the highest risk, particularly when combined with severe findings from automated image analysis.
A more efficient and scalable approach to cervical cancer screening
The findings have proved the efficacy of the strategy that combines self-sampling, HPV extended genotyping and AVE in detecting CIN3+ lesions. The model can improve the efficiency of cervical cancer screening by enabling risk-based management. This is particularly relevant in low-resource settings, where large numbers of HPV-positive women require triage but health care capacity is limited.
"Our results demonstrate that HPV testing and automated visual evaluation provide complementary information that can guide clinical decisions," adds de Sanjosé. The approach also enables self-sampling, rapid testing and real-time image analysis, facilitating a same-day screen–triage–treat model, an important advantage in settings where follow-up visits are difficult.
"Even as HPV vaccination expands globally, effective screening will remain essential for decades, particularly for populations that have not benefited from vaccination," explains Mark Schiffman, epidemiologist at the National Health Institute (NIH) and co-leader of the study. "The PAVE approach offers a feasible solution to extend high-quality cervical cancer screening to underserved populations, contributing to global efforts to reduce cervical cancer incidence and mortality."
More information
Silvia de Sanjosé et al., Extended genotyping of human papillomavirus and AI-assisted cervical visual evaluation for prediction of cervical precancer in nine resource-limited settings (PAVE): a cross-sectional study, The Lancet Obstetrics, Gynaecology, & Women's Health (2026). DOI: 10.1016/s3050-5038(26)00231-1
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Obstetrics & gynecologyWomen's healthOncologyCommon illnesses & Prevention Provided by Barcelona Institute for Global Health Who's behind this story?
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