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. 2021 Feb 25;20(1):110.
doi: 10.1186/s12936-021-03631-3.

Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set

Matthew P Horning  1 , Charles B Delahunt  2   3 , Christine M Bachman  2 , Jennifer Luchavez  4 , Christian Luna  4 , Liming Hu  2 , Mayoore S Jaiswal  5 , Clay M Thompson  6 , Sourabh Kulhare  2 , Samantha Janko  7 , Benjamin K Wilson  2 , Travis Ostbye  2 , Martha Mehanian  2 , Roman Gebrehiwot  5 , Grace Yun  5 , David Bell  8 , Stephane Proux  9 , Jane Y Carter  10 , Wellington Oyibo  11 , Dionicia Gamboa  12 , Mehul Dhorda  13 , Ranitha Vongpromek  14 , Peter L Chiodini  15 , Bernhards Ogutu  16 , Earl G Long  17 , Kyaw Tun  18 , Thomas R Burkot  19 , Ken Lilley  20 , Courosh Mehanian  2
Affiliations

Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set

Matthew P Horning et al. Malar J. .

Abstract

Background: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated.

Methods: The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set.

Results: The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set.

Conclusions: EasyScan GO's expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Keywords: Automated diagnosis; Machine learning; Malaria; Microscopy; WHO.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Contents of ECAMM evaluation slide set. From [16]
Fig. 2
Fig. 2
Malaria detector algorithm architecture. The thick film (left) and thin film (right) malaria detector algorithms each consist of six passes. Multiple branches, each with a paired Pass3-Pass4 block, target different parasite morphologies. A synthesis module (not shown) combines the thick and thin film findings to give a patient diagnosis
Fig. 3
Fig. 3
Quantitation accuracy. EasyScan Go quantitation estimate vs. the reference quantitation. For P. falciparum samples, the ring-stage count is plotted, whereas for non-P. falciparum samples the total count is plotted. The dotted lines show ± 25 % error
See this image and copyright information in PMC

References

    1. WHO. World Malaria Report 2019. Geneva, World Health Organization, 2019.
    1. Zurovac D, Midia B, Ochola SA, English M, Snow RW. Microscopy and outpatient malaria case management among older children and adults in Kenya. Trop Med Int Health. 2006;11:432–40. doi: 10.1111/j.1365-3156.2006.01587.x. - DOI - PubMed
    1. Nazare-Pembele G, Rojas L, Nunez FA. Lack of knowledge regarding the microscopic diagnosis of malaria by technicians of the laboratory network in Luanda, Angola. Biomedica. 2016;36:149–55. - PubMed
    1. Poostchi M, Silamut K, Maude R, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Transl Res. 2018;194:36–55. doi: 10.1016/j.trsl.2017.12.004. - DOI - PMC - PubMed
    1. Delahunt CB, Jaiswal MS, Horning MP, Janko S, Thompson CM, Kulhare S, et al. Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images. In: Proc Global Humanitarian Technologies Conf. 2019;1–8.

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