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ACTIVE NOT RECRUITING
NCT05220163
NA

Evaluating the Impact of Computer-assisted X-ray Diagnosis and Other Triage Tools to Optimise Xpert Orientated Community-based Active Case Finding for TB and COVID-19

Sponsor: University of Cape Town

View on ClinicalTrials.gov

Summary

Tuberculosis (TB) is now the commonest cause of death in many African countries. Globally, \~35% (almost 1 in 3) of TB cases are 'missed' (remain undiagnosed or undetected). In sub-Saharan Africa, 40-50% of the TB case burden remains undiagnosed within the community. These 'missed' TB cases (at primary care level) serve as a reservoir, which severely undermines TB control. With rapid advances in the development of TB screening tests, the investigators aim to determine the pragmatic utility of computer-assisted x-ray diagnosis (CAD). Recent data suggest that CAD performs on par with experienced radiologists to identify potential TB cases, hereby reducing the frequency at which Xpert tests are requested and helps to focus limited resources on the relevant cases. In addition, the investigators aim to test nascent screening technologies for TB diagnosis such as evaluating urine-based TB screening biosignatures. The COVID-19 pandemic has ravaged African peri-urban communities where TB is also common. With the pressing need to improve screening and diagnosis of COVID-19, the investigators plan to explore the potential for urine- and blood-based COVID-19 screening assays. Symptoms of COVID-19 and TB overlap, and limited affordability, as well as the stigma associated with both diseases, severely limits testing. Data are now urgently needed about the feasibility of co-screening and testing for TB and COVID-19. The utility of such an approach, if any, has not been studied in African communities.

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

INTERVENTIONAL

Enrollment

26200

Start Date

2022-02-23

Completion Date

2025-12

Last Updated

2025-09-25

Healthy Volunteers

Yes

Interventions

DIAGNOSTIC_TEST

CAD

It is an artificial intelligence (AI) system for detection of TB on CXR images. The system input is a frontal CXR, and the outputs are 1) a heatmap indicating suspicious regions on the image; and 2) a score (0-100) which implies the likelihood that the x-ray image shows TB.

DIAGNOSTIC_TEST

Xpert

A novel diagnostic for active case finding (GeneXpert MTB/RIF) for TB on sputum collected and performed at POC in a mobile van.

Locations (3)

University of Cape Town

Cape Town, Western Cape, South Africa

Helen Ayles

Lusaka, Zambia

Junior Mutsvangwa

Harare, Zimbabwe