Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.
Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.
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DOI：
10.1186/s12936-023-04780-3
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发表时间：
2023-11-10
期刊：
影响因子：
3
通讯作者：
Okumu, Fredros
中科院分区：
文献类型：
--
作者：
Mshani, Issa H.;Siria, Doreen J.;Mwanga, Emmanuel P.;Sow, Bazoumana BD.;Sanou, Roger;Opiyo, Mercy;Sikulu-Lord, Maggy T.;Ferguson, Heather M.;Diabate, Abdoulaye;Wynne, Klaas;Gonzalez-Jimenez, Mario;Baldini, Francesco;Babayan, Simon A.;Okumu, Fredros
关键词：
近年来，红外（IR）光谱和机器学习（ML）在公共卫生领域的应用研究大大增加。这些技术在测量疟疾的关键参数方面显示出巨大的潜力，疟疾每年仍造成约2.5亿例病例和62万人死亡。多项研究表明，红外光谱和机器学习（ML）的结合可以在实验室和现场调查中准确预测疟疾的流行病学相关参数。现在，疟疾的应用包括确定蚊子媒介的年龄、物种和吸血历史，以及检测人类和蚊子的疟原虫感染。由于世界卫生组织鼓励疟疾流行国家改善其监测响应策略，因此考虑IR和ML技术是否可能满足相关的可行性和成本效益要求以及如何最好地部署它们至关重要。本文综述了红外光谱和ML方法在实地调查和实验室环境中调查疟疾指标的当前应用，并确定了与这些应用相关的关键研究差距。此外，本文还提出了在开发或测试这些技术以供低收入环境使用时应考虑的初步目标产品概况（TPP）。
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.
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影响因子：
4
作者：
Bell, AS;Ranford-Cartwright, LC
通讯作者：
Ranford-Cartwright, LC
影响因子：
4.5
作者：
Abuaku B;Amoah LE;Peprah NY;Asamoah A;Amoako EO;Donu D;Adu GA;Malm KL
通讯作者：
Malm KL
DOI：
10.4269/ajtmh.2003.69.200
发表时间：
2003-08-01
影响因子：
3.3
作者：
Cohuet, A;Simard, F;Fontenille, D
通讯作者：
Fontenille, D
影响因子：
2.1
作者：
BEIER, JC;PERKINS, PV;KOECH, DK
通讯作者：
KOECH, DK
影响因子：
7.4
作者：
Adegoke, John A.;De Paoli, Amanda;Wood, Bayden R.
通讯作者：
Wood, Bayden R.