Validation of Artificial Intelligence Enabled TB Screening and Diagnosis in Zambia

Centre for Infectious Disease Research in Zambia2,432 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

2,432

Conditions

Tuberculosis

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (3)

Chainda South Health Facility

Lusaka, Lusaka Province, Zambia

Chawama first level hospital

Lusaka, Lusaka Province, Zambia

Kanyama level 1

Lusaka, Lusaka Province, Zambia

Outcomes

Primary Outcomes

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.

Time frame: 2 months

Main Cross Sectional Group

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

Time frame: 7 months

Secondary Outcomes

Main Cross Sectional Group:

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

Time frame: 7 months

Main Cross Sectional Group

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

Time frame: 7 months