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

Malaria Journal

Research

Fayad O. Mohammed¹  Muzamil Abdel Hamid¹  Abdelrahim O. Mohamed²  Xavier C. Ding³  Sabine Dittrich³  Seda Yerlikaya³  Ewurama D.A. Owusu⁴  Feng Yang⁵  Hang Yu⁵  Yasmin M. Kassim⁵  Stefan Jaeger⁵  Richard J. Maude⁶ 

[1] Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, Medical Campus, University of Khartoum, Khartoum, Sudan;Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, Medical Campus, University of Khartoum, Khartoum, Sudan;Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan;FIND, Geneva, Switzerland;FIND, Geneva, Switzerland;Department of Medical Laboratory Sciences, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana;Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA;Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand;Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK;Harvard TH Chan School of Public Health, Harvard University, Boston, USA;

关键词: Malaria microscopy;    Computer-aided diagnosis;    Automated screening;    Machine learning;    Field testing;    Smartphone application;   

DOI  :  10.1186/s12936-023-04446-0

 received in 2022-07-08, accepted in 2023-01-06,  发布年份 2023

来源: Springer

【 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.

【 授权许可】

CC BY   
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

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