Validation of Artificial Intelligence Enabled TB Screening and Diagnosis in Zambia

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05139940

Enrollment

2432

Registered

2021-12-01

Start date

2021-11-22

Completion date

2022-11-30

Last updated

2025-05-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Tuberculosis

Keywords

Screening, Diagnosis, Artificial Intelligence, Chest X-ray

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Pilot Group to calibrate the operating points for AI algorithms	2 months	1\. Operating point selection for TB AI algorithm and Abnormal/Normal AI algorithm on CXRs for outcomes listed in Main Cross Sectional Group.
Main Cross Sectional Group	7 months	1\. TB AI algorithm sensitivity and specificity in detecting active TB on CXR compared to panel of radiologists

Secondary

Measure	Time frame	Description
Main Cross Sectional Group:	7 months	1\. TB AI algorithm sensitivity and specificity in detecting active TB compared to World Health Organisation (WHO) performance guidelines of 90% sensitivity and 70% specificity
Main Cross Sectional Group	7 months	2\. Abnormal/Normal AI algorithm sensitivity and specificity compared to 90% sensitivity and 50% specificity.

Countries

Zambia

Outcome results