Browsing by Author "Vo LNQ"

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    Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.
    (2023-Dec-21) Creswell J; Vo LNQ; Qin ZZ; Muyoyeta M; Tovar M; Wong EB; Ahmed S; Vijayan S; John S; Maniar R; Rahman T; MacPherson P; Banu S; Codlin AJ; Department of Global Health, WHO Collaboration Centre On Tuberculosis and Social Medicine, Karolinska Institutet, Stockholm, Sweden.; Stop TB Partnership, Geneva, Switzerland.; Friends for International TB Relief (FIT), Hanoi, Vietnam.; Division of Infectious Diseases, Heersink School of Medicine, University of Alabama Birmingham, Birmingham, AL, USA.; Africa Health Research Institute, KwaZulu-Natal, South Africa.; PATH India, Mumbai, India.; Socios En Salud Sucursal Peru, Lima, Peru.; Interactive Research and Development (IRD) Pakistan, Karachi, Pakistan.; London School of Hygiene & Tropical Medicine, London, UK.; Janna Health Foundation, Yola, Nigeria.; International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.; Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi.; Stop TB Partnership, Geneva, Switzerland. jacobc@stoptb.org.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; School of Health & Wellbeing, University of Glasgow, Glasgow, UK.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world's deadliest diseases. Most deaths are among persons with TB who are not reached with diagnosis and treatment. Thus, timely screening and accurate detection of TB, particularly using sensitive tools such as chest radiography, is crucial for reducing the global burden of this disease. However, lack of qualified human resources represents a common limiting factor in many high TB-burden countries. Artificial intelligence (AI) has emerged as a powerful complement in many facets of life, including for the interpretation of chest X-ray images. However, while AI may serve as a viable alternative to human radiographers and radiologists, there is a high likelihood that those suffering from TB will not reap the benefits of this technological advance without appropriate, clinically effective use and cost-conscious deployment. The World Health Organization recommended the use of AI for TB screening in 2021, and early adopters of the technology have been using the technology in many ways. In this manuscript, we present a compilation of early user experiences from nine high TB-burden countries focused on practical considerations and best practices related to deployment, threshold and use case selection, and scale-up. While we offer technical and operational guidance on the use of AI for interpreting chest X-ray images for TB detection, our aim remains to maximize the benefit that programs, implementers, and ultimately TB-affected individuals can derive from this innovative technology.
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    Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries.
    (2024) Codlin AJ; Vo LNQ; Garg T; Banu S; Ahmed S; John S; Abdulkarim S; Muyoyeta M; Sanjase N; Wingfield T; Iem V; Squire B; Creswell J; Stop TB Partnership, Geneva, Switzerland.; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.; Karolinska Institutet, Stockholm, Sweden.; Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom.; Janna Health Foundation, Yola, Nigeria.; icddr,b, Dhaka, Bangladesh.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; Friends for International TB Relief, Hanoi, Viet Nam.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s44263-024-00081-2.