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Good practice

AVE — AI-Assisted Cervical Precancer Screening in Zambia's Public Health Facilities

Zambia · Lusaka · See the Zambia profile

A Zambia Ministry of Health study screened 8,204 women at eight public clinics with an AI tool reading cervical images, hitting 85% sensitivity/86% specificity; a five-country African trial roughly doubled detection sensitivity over standard visual inspection.

0.91
AUC for detecting CIN2+ lesions (Zambia validation) (Oct 2019 - Dec 2021)
85 % (95% CI 81-90%)
Sensitivity for detecting CIN2+ lesions (Zambia validation) (Oct 2019 - Dec 2021)
86 % (95% CI 84-88%)
Specificity for detecting CIN2+ lesions (Zambia validation) (Oct 2019 - Dec 2021)
8,204
Women enrolled in the Zambia validation study (Oct 2019 - Dec 2021)
60.1 % (95% CI 55.5-64.5)
AVE sensitivity for CIN2+ detection (five-country study) (Mar 2022 - Jan 2023)
36.6 % (95% CI 32.2-41.1)
VIA sensitivity for CIN2+ detection (five-country study) (Mar 2022 - Jan 2023)

Details

Maturity
Pilot
Promoter
Zambia Ministry of Health, with the U.S. National Cancer Institute, Global Health Labs, and Unitaid
Period
October 2019 - December 2021 (Zambia validation); five-country regional study March 2022 - January 2023
Keywords
health screening, cervical cancer, computer vision, public health service delivery

Context

Automated Visual Evaluation (AVE) is an AI image-classification tool, originally developed with the U.S. National Cancer Institute, that analyses smartphone or digital-camera photographs of the cervix to flag possible precancerous lesions, intended to assist rather than replace the health worker performing the exam. Between October 2019 and December 2021, Zambia's Ministry of Health, working with NCI, Global Health Labs and Unitaid, ran an internal validation study across eight public health facilities (seven in Lusaka, one in Kitwe), enrolling 8,204 women aged 25-55 through the country's public-sector cervical cancer prevention programme.

Objectives

Assess how accurately AVE identifies histology-confirmed precancerous and cancerous cervical lesions (CIN2+) in a real public-health-facility setting, and compare its diagnostic performance against standard visual inspection with acetic acid (VIA).

Activities

The Zambia study confirmed AVE results against histopathology for the 8,204 enrolled women. A separate prospective, observational diagnostic-accuracy study ran across government health facilities in Malawi, Rwanda, Senegal, Zambia and Zimbabwe between March 2022 and January 2023, comparing AVE's detection of CIN2+ lesions against naked-eye VIA.

Results

In Zambia, AVE identified CIN2+ lesions with an AUC of 0.91, translating to 85% sensitivity (95% CI 81-90%) and 86% specificity (95% CI 84-88%). In the five-country study, AVE lifted sensitivity for detecting CIN2+ lesions to 60.1% (95% CI 55.5-64.5), compared with 36.6% (95% CI 32.2-41.1) for VIA alone, with an accepted trade-off in specificity.

Conclusions

Both studies were explicitly framed as diagnostic-accuracy research rather than a finished national screening rollout; the study authors say the specificity trade-off makes clinician oversight of every AI-flagged case essential, and no source found describes AVE as deployed beyond the study facilities.

Implementation

Indicative cost
Medium (€50k–€500k) — Not itemised in the source; funded via international research grants (including NCI grant UH3CA202721) rather than domestic budget alone, and dependent on smartphone/camera equipment and histopathology confirmation infrastructure at study facilities.
Time to results
Medium (1–3 years) — Zambia validation ran October 2019 to December 2021; the five-country diagnostic-accuracy study ran March 2022 to January 2023.
Staffing & skills
Health workers performing cervical exams, supported by AVE image analysis, Zambia Ministry of Health public-sector cervical cancer prevention programme staff, Technical partners: U.S. National Cancer Institute, Global Health Labs, Unitaid

Conditions for success

  • Histopathology confirmation infrastructure to validate AI-flagged results
  • Clinician oversight of every AI-flagged case, given the specificity trade-off
  • International grant funding (including NCI grant UH3CA202721)

Common failure modes

  • Lower specificity than sensitivity means false positives require clinician review
  • No published evidence yet of deployment beyond the study facilities
  • No documented national ownership or financing plan for scale-up after the studies

Where it fits

Governance type
national ministry of health with international research-funder support
Scale
sub-national pilot facilities across one to five African countries
Income level
low-income and lower-middle-income

Data sources

Where this practice's information was retrieved from, and when.

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