Patient-level performance evaluation of a smartphone-based malaria diagnostic application

被引:7
|
作者
Yu, Hang [1 ]
Mohammed, Fayad O. [2 ]
Abdel Hamid, Muzamil [2 ]
Yang, Feng [1 ]
Kassim, Yasmin M. [1 ]
Mohamed, Abdelrahim O. [2 ,3 ]
Maude, Richard J. [4 ,5 ,6 ]
Ding, Xavier C. [7 ]
Owusu, Ewurama D. A. [7 ,8 ]
Yerlikaya, Seda [7 ]
Dittrich, Sabine [7 ]
Jaeger, Stefan [1 ]
机构
[1] NIH, Lister Hill Natl Ctr Biomed Commun, Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA
[2] Univ Khartoum, Inst Endem Dis, Dept Parasitol & Med Entomol, Med Campus, Khartoum, Sudan
[3] Univ Khartoum, Fac Med, Dept Biochem, Khartoum, Sudan
[4] Mahidol Univ, Fac Trop Med, Mahidol Oxford Trop Med Res Unit, Bangkok, Thailand
[5] Univ Oxford, Ctr Trop Med & Global Hlth, Nuffield Dept Med, Oxford, England
[6] Harvard Univ, Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA
[7] FIND, Geneva, Switzerland
[8] Univ Ghana, Coll Hlth Sci, Sch Biomed & Allied Hlth Sci, Dept Med Lab Sci, Accra, Ghana
基金
英国惠康基金; 美国国家卫生研究院;
关键词
Malaria microscopy; Computer-aided diagnosis; Automated screening; Machine learning; Field testing; Smartphone application; THICK BLOOD SMEARS; PARASITES;
D O I
10.1186/s12936-023-04446-0
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
BackgroundMicroscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.MethodsA total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.ResultsMalaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.ConclusionMalaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.
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页数:10
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