Validation of Artificial Intelligence Enabled TB Screening and Diagnosis in Zambia

NCT05139940 | Completed

• 1 other identifier: Google AI
• Study Type: observational
• Target: 2,432
• Locations: 1 country
• Sites: 3

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

Keywords

Screening; Diagnosis; Artificial Intelligence; Chest X-ray

Outcome Measures

Primary Outcomes (2)

• Pilot Group to calibrate the operating points for AI algorithms

  1\. Operating point selection for TB AI algorithm and Abnormal/Normal AI algorithm on CXRs for outcomes listed in Main Cross Sectional Group.

  2 months

• Main Cross Sectional Group

  1\. TB AI algorithm sensitivity and specificity in detecting active TB on CXR compared to panel of radiologists

  7 months

Secondary Outcomes (2)

• Main Cross Sectional Group:

  7 months

• Main Cross Sectional Group

  7 months

Study Arms (2)

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.

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study will be conducted from Chainda South Clinic, Chawama and Kanyama General Hospitals. These facilities are selected based on existing access to digital radiography. Participants will be drawn from patients attending these health facilities for: * Health services * TB households of those who are close contacts of TB households within the catchment area of these facilities

You may qualify if:

• 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.

Sponsors & Collaborators

• Centre for Infectious Disease Research in Zambia: lead

Study Sites (3)

Chainda South Health Facility

Lusaka, Lusaka Province, 10101, Zambia

Location

Chawama first level hospital

Lusaka, Lusaka Province, 10101, Zambia

Location

Kanyama level 1

Lusaka, Lusaka Province, 10101, Zambia

Location

MeSH Terms

Conditions

Tuberculosis; Disease

Condition Hierarchy (Ancestors)

Mycobacterium Infections; Actinomycetales Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 18, 2021
• First Posted: December 1, 2021
• Study Start: November 22, 2021
• Primary Completion: September 30, 2022
• Study Completion: November 30, 2022
• Last Updated: May 15, 2025

Record last verified: 2025-05

Locations

ZA (3)