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OpenTrials
Completed

NCT Number: NCT05139940

Validation of Artificial Intelligence Enabled TB Screening and Diagnosis in Zambia

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.

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Key information

Conditions

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

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Chainda South Health Facility, Lusaka, Lusaka Province, Zambia

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Exclusion criteria

  • Individuals who do meet the above inclusion criteria will be excluded. In addition, individuals with history of TB treatment within 365 days prior to enrolment will be excluded.

Treatment and study plan

Primary outcomes

  1. Pilot Group to calibrate the operating points for AI algorithms

    Time frame: 2 months

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

  2. Main Cross Sectional Group

    Time frame: 7 months

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

Secondary outcomes

  1. Main Cross Sectional Group:

    Time frame: 7 months

    • TB AI algorithm sensitivity and specificity in detecting active TB compared to World Health Organisation (WHO) performance guidelines of 90% sensitivity and 70% specificity

  2. Main Cross Sectional Group

    Time frame: 7 months

    • Abnormal/Normal AI algorithm sensitivity and specificity compared to 90% sensitivity and 50% specificity.

Sponsors and collaborators

Lead sponsor

Centre for Infectious Disease Research in Zambia

Other

Registry information

Important dates

Study start
2021
Primary completion
2022
Study completion
2022
First posted
Dec 1, 2021
Registry last updated
May 15, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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