Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 6;1(1):17.
doi: 10.1186/s44263-023-00017-2.

Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria

Stephen John  1 , Suraj Abdulkarim  2 , Salisu Usman  3 , Md Toufiq Rahman  4 , Jacob Creswell  5
Affiliations

Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria

Stephen John et al. BMC Glob Public Health. .

Abstract

Background: Ultra-portable X-ray devices with artificial intelligence (AI) are increasingly used to screen for tuberculosis (TB). Few studies have documented their performance. We aimed to evaluate the performance of chest X-ray (CXR) and symptom screening for active case finding of TB among remote populations using ultra-portable X-ray and AI.

Methods: We organized screening camps in rural northeast Nigeria, and all consenting individuals ≥ 15 years were screened for TB symptoms (cough, fever, night sweats, and weight loss) and received a CXR. We used a MinXray Impact system interpreted by AI (qXR V3), which is a wireless setup and can be run without electricity. We collected sputum samples from individuals with an qXR abnormality score of 0.30 or higher or if they reported any TB symptoms. Samples were tested with Xpert MTB/RIF. We documented the TB screening cascade and evaluated the performance of screening with different combinations of symptoms and CXR interpreted by AI.

Results: We screened 5297 individuals during 66 camps: 2684 (51%) were females, and 2613 (49%) were males. Using ≥ 2 weeks of cough to define presumptive TB, 1056 people (20%) would be identified. If a cough of any duration was used, the number with presumptive TB increased to 1889 (36%) and to 3083 (58%) if any of the four symptoms were used. Overall, 769 (14.5%) had abnormality scores of 0.3 or higher, and 447 (8.4%) had a score of 0.5 or higher. We collected 1021 samples for Xpert testing and detected 85 (8%) individuals with TB. Screening for prolonged cough only identified 40% of people with TB. Any symptom detected 90.6% of people with TB, but specificity was 11.4%. Using an AI abnormality score of 0.50 identified 89.4% of people with TB with a specificity of 62.8%.

Conclusions: Ultra-portable CXR can be used to provide more efficient TB screening in hard-to-reach areas. Symptom screening missed large proportions of people with bacteriologically confirmed TB. Employing AI to read CXR can improve triaging when human readers are unavailable and can save expensive diagnostic testing costs.

Keywords: Active case finding; Artificial intelligence; Chest X-ray; Nigeria; Tuberculosis; Ultra-portable.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study protocol was reviewed and approved by Adamawa (no. 2022069) and Gombe State Research Ethical Committees in the Ministry of Health (no. GMEHREC/SMOH/2022/027). Verbal consent was provided by all participants as a large proportion of participants were illiterate. For those younger than 18, parental consent was given. The study adhered to the tenets of the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: J.C. and T.R. work at the Stop TB Partnership Secretariat. They do not make funding decisions but provide technical support to selected projects.

Figures

Fig. 1
Fig. 1
Study flow chart
See this image and copyright information in PMC

References

    1. World Health Organization. Global tuberculosis report 2022. Programme GT; 2022 27 October 2022. Contract No.: ISBN: 978–92–4–006172–9.
    1. Wells W. Onions and prevalence surveys: how to analyze and quantify tuberculosis case-finding gaps. Int J Tuberc Lung Dis. 2017;21(11):1101–13. - PubMed
    1. Titahong CN, Ayongwa GN, Waindim Y, Nguafack D, Kuate AK, Wandji IAG, et al. Patient-pathway analysis of tuberculosis services in Cameroon. Trop Med Infect Dis. 2021;6(4):171. - PMC - PubMed
    1. Claassens M, Jacobs E, Cyster E, Jennings K, James A, Dunbar R, et al. Tuberculosis cases missed in primary health care facilities: should we redefine case finding? Int J Tuberc Lung Dis. 2013;17(5):608–14. - PubMed
    1. Ananthakrishnan R, Thiagesan R, Auguesteen S, Karunakaran N, Jayabal L, Stevens R, et al. The impact of chest radiography and Xpert MTB/RIF testing among household contacts in Chennai, India. PLoS ONE. 2020;15(11):e0241203. - PMC - PubMed

LinkOut - more resources