Performance evaluation of different strategies based on microscopy techniques, rapid diagnostic test and molecular loop-mediated isothermal amplification assay for the diagnosis of imported malaria

Charpentier, Éléna; Bénichou, Emmanuel; Pagès, Arnaud; Chauvin, Pamela; Fillaux, Judith; Valentin, Alexis; Guegan, Hélène; Guemas, Emilie; Salabert, Anne‐Sophie; Armengol, Carolina; Ménard, Sandie; Cassaing, Sophie; Berry, Antoine; Iriart, Xavier

doi:10.1016/j.cmi.2019.05.010

Cited by 40 publications

(45 citation statements)

References 19 publications

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“…A hypothesis to explain the emergence of falsely negative RDTs would be a reduction in quality of the tests at the stage of manufacture since 2017 but this has not been reported. One could also hypothesize that the introduction of a molecular method, more sensitive than microscopy and antigenic tests [14][15][16][17][18], has allowed detecting low parasitaemia infections for which the 'classical' RDT plus microscopy-based strategy may yield negative results. However, according to the records on the 'pre-LAMP' period, and given that the Academic Hospital deals with most malaria cases in the area, no patient with negative laboratory tests developed symptomatic malaria, suggesting that, if any, misdiagnosed patients have cured spontaneously.…”

Section: Discussionmentioning

confidence: 99%

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Rapid diagnostic tests failing to detect infections by Plasmodium falciparum encoding pfhrp2 and pfhrp3 genes in a non-endemic setting

Pasquier

Azoury

Sasso

et al. 2020

Malar J

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Background: Rapid diagnostic tests (RDTs) detecting the histidine-rich protein 2 (PfHRP2) have a central position for the management of Plasmodium falciparum infections. Yet, variable detection of certain targeted motifs, low parasitaemia, but also deletion of pfhrp2 gene or its homologue pfhrp3, may result in false-negative RDT leading to misdiagnosis and delayed treatment. This study aimed at investigating the prevalence, and understanding the possible causes, of P. falciparum RDT-negative infections at Montpellier Academic Hospital, France. Methods: The prevalence of falsely-negative RDT results reported before and after the introduction of a loop-mediated isothermal amplification (LAMP) assay, as part as the malaria screening strategy in January 2017, was analysed. Negative P. falciparum RDT infections were screened for pfhrp2 or pfhrp3 deletion; and exons 2 were sequenced to show a putative genetic diversity impairing PfHRP2 detection. Results: The overall prevalence of P. falciparum negative RDTs from January 2006 to December 2018 was low (3/446). Whereas no cases were reported from 2006 to 2016 (0/373), period during which the malaria diagnostic screen was based on microscopy and RDT, prevalence increased up to 4.1% (3/73) between 2017 and 2018, when molecular detection was implemented for primary screening. Neither pfhrp2/3 deletion nor major variation in the frequency of repetitive epitopes could explain these false-negative RDT results. Conclusion: This paper demonstrates the presence of pfhrp2 and pfhrp3 genes in three P. falciparum RDT-negative infections and reviews the possible reasons for non-detection of HRP2/3 antigens in a non-endemic setting. It highlights the emergence of falsely negative rapid diagnostic tests in a non-endemic setting and draws attention on the risk of missing malaria cases with low parasitaemia infections using the RDT plus microscopy-based strategy currently recommended by French authorities. The relevance of a novel diagnostic scheme based upon a LAMP assay is discussed.

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Section: Discussionmentioning

confidence: 99%

“…Alternative diagnostic approaches include molecular methods which display high sensitivity but are generally technically demanding and time consuming, thus not suitable for urgent diagnosis [13]. In this context, loop-mediated isothermal amplification (LAMP) assays have proven highly effective for rapid Plasmodium screening [14][15][16][17][18].…”

mentioning

confidence: 99%

Rapid diagnostic tests failing to detect infections by Plasmodium falciparum encoding pfhrp2 and pfhrp3 genes in a non-endemic setting

Pasquier

Azoury

Sasso

et al. 2020

Malar J

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“…These methods are expensive and often infeasible to implement in low-resource settings due to the required equipment and use of trained technicians (CDC, 2020). In low-resource settings, the use of light microscopy on a thin or thick blood smear with Giemsa stain is often used to confirm the presence of malaria parasites (Charpentier et al, 2020). Infection severity is frequently measured through the percentage of red blood cells infected with malaria parasites, also known as percent parasitemia or parasitemia burden.…”

Section: Malaria In Developing Countriesmentioning

confidence: 99%

Convolutional neural networks to automate the screening of malaria in low-resource countries

Zhao

Kolluri

Anand

et al. 2020

PeerJ

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Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.

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“…As most malaria cases occur in rural areas (1), the disease burden is caused partly by difficulties in diagnosing the disease. Currently, multiple techniques exist for malaria diagnostics (2). Light microscopy assessment of blood smears to detect Plasmodium .…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Holmström

Stenman

Suutala

et al. 2020

Preprint

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Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.

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Performance evaluation of different strategies based on microscopy techniques, rapid diagnostic test and molecular loop-mediated isothermal amplification assay for the diagnosis of imported malaria

Cited by 40 publications

References 19 publications

Rapid diagnostic tests failing to detect infections by Plasmodium falciparum encoding pfhrp2 and pfhrp3 genes in a non-endemic setting

Rapid diagnostic tests failing to detect infections by Plasmodium falciparum encoding pfhrp2 and pfhrp3 genes in a non-endemic setting

Convolutional neural networks to automate the screening of malaria in low-resource countries

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

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