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Validation of Artificial Intelligence as Decision Support System in VIA (PRESCRIP-TEC)

NCT06452004Active, Not RecruitingNAINTERVENTIONAL

Summary

The research project is a component of another research project that applies the protocol of the World Health Organization for screening of cervical cancer, with testing of high-risk Human Papilloma Virus (hrHPV) as first screening. In the screen, triage and treat approach women who tested positive for hrHPV are undergoing Visual Inspection of the cervix with Acetic Acid (VIA). This procedure is applied in Uganda, India and Bangladesh. However the quality of VIA by lower-trained staff is variable because Low and Middle Income Countries face limited numbers of qualified health care professionals. Artificial intelligence (AI) might be a solution to improve consistency of VIA assessment. This research validates an AI decision support system (AI-DSS) under field conditions.

Key Facts

Lead Sponsor

University Medical Center Groningen

Enrollment

600

Start Date

2022-10-01

Completion Date

2024-12-31

Study Type

INTERVENTIONAL

Official Title

Prevention and Screening Intervention Project - Towards Elimination of Cervical Cancer: Validation of Artificial Intelligence as Decision Support System in VIA

Interventions

Validation of AI-DSS in BangladeshValidation of AI-DSS in UgandaValidation of AI-DSS in India

Conditions

HPV InfectionCervix CancerCervical Dysplasia

Eligibility

Age Range

30 Years – 60 Years

Sex

FEMALE

Inclusion Criteria:

* Female in eligible age group
* Women who tested HPV positive and are eligible for VIA
* Ability to give informed consent and participate in study

Exclusion Criteria:

* Clinical signs of cervical carcinoma
* Menstruation or other vaginal blood loss
* HPV negative women

Outcome Measures

Primary Outcomes

Accuracy, sensitivity, specificity of AI-DSS

Measurement of true and false positive and true and false negative results of the AI DSS in relation to assessment by expert panel

Time frame: through study completion, estimated 2 years

Secondary Outcomes

Percentage of quality pictures

Percentage of pictures taken during the VIA procedures, which meet according to the expert panel the quality standards suitable for AI-DSS assessment, based on visibility of cervix and Squamo-Columnar Junction (SCJ), light, reflection of speculum, mucus-free surface

Time frame: through study completion, estimated 2 years

Locations

Mpasana, Kakumiro, Uganda

Linked Papers

2023-10-02

Artificial intelligence and visual inspection in cervical cancer screening

Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm. A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values. Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively. This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

2022-07-15

Investigating feasibility of 2021 WHO protocol for cervical cancer screening in underscreened populations: PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC)

Abstract Background High-risk human papillomavirus (hrHPV) testing has been recommended by the World Health Organization as the primary screening test in cervical screening programs. The option of self-sampling for this screening method can potentially increase women’s participation. Designing screening programs to implement this method among underscreened populations will require contextualized evidence. Methods PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC) will use a multi-method approach to investigate the feasibility of implementing a cervical cancer screening strategy with hrHPV self-testing as the primary screening test in Bangladesh, India, Slovak Republic and Uganda. The primary outcomes of study include uptake and coverage of the screening program and adherence to follow-up. These outcomes will be evaluated through a pre-post quasi-experimental study design. Secondary objectives of the study include the analysis of client-related factors and health system factors related to cervical cancer screening, a validation study of an artificial intelligence decision support system and an economic evaluation of the screening strategy. Discussion PRESCRIP-TEC aims to provide evidence regarding hrHPV self-testing and the World Health Organization’s recommendations for cervical cancer screening in a variety of settings, targeting vulnerable groups. The main quantitative findings of the project related to the impact on uptake and coverage of screening will be complemented by qualitative analyses of various determinants of successful implementation of screening. The study will also provide decision-makers with insights into economic aspects of implementing hrHPV self-testing, as well as evaluate the feasibility of using artificial intelligence for task-shifting in visual inspection with acetic acid. Trial registration ClinicalTrials.gov, NCT05234112. Registered 10 February 2022

Linked Investigators

Carolyn Nakisige

Marat Sultanov