Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

Codlin, Andrew James, Vo, Luan Nguyen Quang, Garg, Tushar, Banu, Sayera, Ahmed, Shahriar, John, Stephen, Abdulkarim, Suraj, Muyoyeta, Monde, Sanjase, Nsala, Wingfield, Tom ORCID: https://orcid.org/0000-0001-8433-6887, Iem, Vibol, Squire, Bertie ORCID: https://orcid.org/0000-0001-7173-9038 and Creswell, Jacob (2024) 'Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries'. BMC Global and Public Health, Vol 2, Issue 1, e52.

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Abstract

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.
Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.
Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.
Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

Item Type:	Article
Subjects:	WA Public Health > WA 30 Socioeconomic factors in public health (General) WF Respiratory System > Tuberculosis > WF 200 Tuberculosis (General) WF Respiratory System > Tuberculosis > WF 220 Diagnosis. Prognosis
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1186/s44263-024-00081-2
SWORD Depositor:	JISC Pubrouter
Depositing User:	JISC Pubrouter
Date Deposited:	15 Aug 2024 14:26
Last Modified:	15 Aug 2024 14:26
URI:	https://archive.lstmed.ac.uk/id/eprint/25042

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