Validation of Artificial Intelligence Enabled TB Screening and Diagnosis in Zambia

NCT ID: NCT05139940

Last Updated: 2025-05-15

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 2432 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-11-22
• Study Completion Date: 2022-11-30

Brief Summary

Tuberculosis (TB) is a global epidemic and for many years has remained a major cause of death throughout the developing world. Zambia is among the top 30 TB/HIV high burden countries. Chest X-ray (CXR) is recommended as a triaging test for TB, and a diagnostic aid when available. However, many high-burden settings lack access to experienced radiologists capable of interpreting these images, resulting in mixed sensitivity, poor specificity, and large inter-observer variation. In recognition of this challenge, the World Health Organization has recommended the use of automated systems that utilize artificial intelligence (AI) to read CXRs for screening and triaging for TB. In this study, we primarily evaluate the performance of our AI algorithm for TB, and secondarily for Abnormal/Normal.

Conditions

Tuberculosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Pilot Group to calibrate the operating points for AI algorithms (Estimated Enrollment up to 500)

Diagnostic Test: TB AI algorithm performance in detecting active TB.

Diagnostic Test: TB diagnosis from sputum and urine (Smear microscopy, Xpert MTB RIF/ultra, Lipoarabinomannan (LAM) and mycobacterial culture)

Diagnostic Test: Abnormal/Normal AI algorithm to detect abnormal/normal CXRs.

Diagnostic Test: Radiologist evaluation of CXRs for active TB, abnormal/normal.

Diagnostic Test: Labs: Hemoglobin level, HIV status, CD4 count.

No interventions assigned to this group

Main Cross Sectional Group (Estimated Enrollment 1932 minus the volume in pilot)

Diagnostic Test: TB AI algorithm performance in detecting active TB.

Diagnostic Test: TB diagnosis from sputum and urine (Smear microscopy, Xpert MTB RIF/ultra, Lipoarabinomannan (LAM) and mycobacterial culture)

Diagnostic Test: Abnormal/Normal AI algorithm to detect abnormal/normal CXRs.

Diagnostic Test: Radiologist evaluation of CXRs for active TB, abnormal/normal.

Diagnostic Test: Labs: Hemoglobin level, HIV status, CD4 count.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Participants who are 18 years and older with a known HIV status or are willing to undergo HIV testing if unknown HIV status and meet the following criteria will be included in the study:

• Presumptive TB patients defined as having any of the following:

○ Cough, Weight loss, Night sweats, Fever

• Household /close TB contacts regardless of symptoms
• Newly diagnosed HIV regardless of symptoms.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Centre for Infectious Disease Research in Zambia

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Chainda South Health Facility

Lusaka, Lusaka Province, Zambia

Chawama first level hospital

Lusaka, Lusaka Province, Zambia

Kanyama level 1

Lusaka, Lusaka Province, Zambia

Countries

Zambia

Other Identifiers

Google AI

Identifier Type: -

Identifier Source: org_study_id