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Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.

التفاصيل البيبلوغرافية
العنوان: Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.
المؤلفون: Mshani IH; Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania. imshani@ihi.or.tz.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK. imshani@ihi.or.tz., Siria DJ; Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK., Mwanga EP; Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK., Sow BB; Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso., Sanou R; Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso., Opiyo M; Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.; Malaria Elimination Initiative (MEI), Institute for Global Health Sciences, University of California, San Francisco, USA., Sikulu-Lord MT; Faculty of Science, School of the Environment, The University of Queensland, Brisbane, QLD, Australia., Ferguson HM; Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK., Diabate A; Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso., Wynne K; School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK., González-Jiménez M; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.; School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK., Baldini F; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK., Babayan SA; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK. Simon.Babayan@glasgow.ac.uk., Okumu F; Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania. fredros@ihi.or.tz.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK. fredros@ihi.or.tz.; School of Life Sciences and Biotechnology, Nelson Mandela African Institution of Science and Technology, Arusha, United Republic of Tanzania. fredros@ihi.or.tz.; School of Public Health, The University of the Witwatersrand, Park Town, South Africa. fredros@ihi.or.tz.
المصدر: Malaria journal [Malar J] 2023 Nov 10; Vol. 22 (1), pp. 346. Date of Electronic Publication: 2023 Nov 10.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: BioMed Central Country of Publication: England NLM ID: 101139802 Publication Model: Electronic Cited Medium: Internet ISSN: 1475-2875 (Electronic) Linking ISSN: 14752875 NLM ISO Abbreviation: Malar J Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : BioMed Central, [2002-
مواضيع طبية MeSH: Malaria*/epidemiology , Culicidae*, Animals ; Humans ; Artificial Intelligence ; Evidence Gaps ; Mosquito Vectors ; Spectrophotometry, Infrared/methods
مستخلص: Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.
(© 2023. The Author(s).)
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معلومات مُعتمدة: MR/P025501/1 United Kingdom MRC_ Medical Research Council
فهرسة مساهمة: Keywords: Artificial intelligence; Deep learning; Infrared spectroscopy; Machine learning; Malaria surveillance; Target product profiles
تواريخ الأحداث: Date Created: 20231111 Date Completed: 20231113 Latest Revision: 20240320
رمز التحديث: 20240320
مُعرف محوري في PubMed: PMC10638832
DOI: 10.1186/s12936-023-04780-3
PMID: 37950315
قاعدة البيانات: MEDLINE
الوصف
تدمد:1475-2875
DOI:10.1186/s12936-023-04780-3