Risk stratification of malaria among school-age children with mHealth spectroscopy of blood analysis

利用血液分析的移动健康光谱对学龄儿童疟疾进行风险分层

• 批准号: 10704123
• 负责人: Young L Kim
• 金额: $ 16.32万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704123
• 关键词: 15 year old; Acute; Africa; Africa South of the Sahara; Algorithms; Android; Anemia; Artemisinins; Biomedical Engineering; Blood; Cause of Death; Cellular Phone; Chemoprevention; Child; Child Health; Collection; Color; Combined Modality Therapy; Community Health Aides; Computational algorithm; Country; Data; Data Collection; Diagnosis; Diagnostic tests; Doctor of Philosophy; Education; Electronic Health Record; Exclusion; Eyelid structure; Fever; Goals; Health; Health Technology; Health care facility; Healthcare Systems; Hemoglobin; Hemoglobin concentration result; Infection; Intervention; Investments; Learning; Machine Learning; Malaria; Malaria Diagnosis; Malaria Diagnostic; Mass Screening; Measurement; Measures; Methods; Mobile Health Application; Modeling; Molecular; Paper; Parasites; Patients; Performance; Phase; Plasmodium falciparum; Play; Public Health; Rapid diagnostics; Reporting; Research; Resource-limited setting; Resources; Risk; Role; Rwanda; School-Age Population; Schools; Spectrum Analysis; Structure of palpebral conjunctiva; Technology; Telemedicine; Test Result; Testing; Undifferentiated; Universities; age group; associated symptom; clinically relevant; cognitive enhancement; cognitive function; cost effective; deep learning; digital; eHealth; electronic health record system; empowerment; human capital; imaging Segmentation; improved; mHealth; malaria infection; malaria transmission; mobile application; response; risk stratification; scale up; screening; standard of care; statistical learning; transmission process; ultra high resolution

PROJECT SUMMARY/ABSTRACT Malaria is one of the most serious public health problems in sub-Saharan Africa. School-age children are most commonly infected with malaria parasites with an estimated 200 million at risk. Malaria screening for school- age children in endemic countries is critical in two aspects: malaria transmission and educational performance (human capital investment). Malaria rapid diagnostic test (RDT)-based interventions have shown to be effective, but mass screening with malaria RDTs on a routine basis is expensive and impractical. As a result, school-age children are often excluded. In this respect, risk stratification (prescreening) for malaria RDTs can play a critical role in the diagnosis and management of malaria. We hypothesize that a combination of blood hemoglobin level and acute undifferentiated febrile illness assessments can risk-stratify school-age children who will benefit from malaria RDTs and avoid unnecessary RDTs. Malaria infections in school-age children are strongly associated with anemia. Thus, noninvasive blood hemoglobin level readings can be highly beneficial for identifying asymptomatic (undetected) afebrile malaria infections. We will take advantage of our recently developed mHealth method that can reliably predict blood hemoglobin levels from digital photographs of the inner eyelid taken by a low-end smartphone. In Aim 1 (R21 phase), we will perfect an mHealth blood hemoglobin computation algorithm applied to school-age children (6 to 15 years of age) in Rwanda. The proposed machine learning approach will hybridize deep learning and statistical learning to accurately and precisely measure blood hemoglobin content among school-age children using an unmodified smartphone. In Aim 2 (R33 phase), we will develop an mHealth risk-stratification model to determine the need of malaria RDTs among school-age children. We will investigate the added value of mHealth blood hemoglobin assessments in identifying patients who will benefit from malaria RDTs and will need confirmatory malaria diagnosis. We will further formulate an advanced risk-stratification model that can forecast molecular test-confirmed malaria. In Aim 3 (R33 phase), we will implement an mHealth application integrating malaria risk stratification with the existing electronic health record (EHR) system. We will incorporate the mHealth technology into an Android- based EHR-integrated mobile application for community health workers (CHWs) and health facilities in our study settings. We will also include a digital reporting platform to replace paper-based patient data collection for CHWs and allow for automatic transmission into the currently used EHR system in our study settings. After successful completion, we expect to improve malaria diagnosis and management among school-age children, by empowering CHWs and health facilities with less hardware-dependent mHealth technologies. The proposed data-driven and connected mHealth technologies can maximize the nationwide scale-up of cost-effective malaria diagnosis and management in Rwanda, potentially offering mobility, simplicity, and affordability for rapid and scalable adaptation in other resources-limited settings.

项目总结/文摘

项目成果

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.

• DOI: 10.1093/pnasnexus/pgad111
• 发表时间: 2023-04
• 期刊: PNAS NEXUS
• 作者: Ji, Yuhyun;Park, Sang Mok;Kwon, Semin;Leem, Jung Woo;Nair, Vidhya Vijayakrishnan;Tong, Yunjie;Kim, Young L.
• 通讯作者: Kim, Young L.