Holmström Oscar, Stenman Sebastian, Suutala Antti, Moilanen Hannu, Kücükel Hakan, Ngasala Billy, Mårtensson Andreas, Mhamilawa Lwidiko, Aydin-Schmidt Berit, Lundin Mikael, Diwan Vinod, Linder Nina, Lundin Johan
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Center of Microscopy and Nanotechnology, University of Oulu, Oulu, Finland.
PLoS One. 2020 Nov 17;15(11):e0242355. doi: 10.1371/journal.pone.0242355. eCollection 2020.
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.
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.
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.
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.
疟疾仍然是一个重大的全球健康问题，需要改进可在现场使用的诊断测试。我们开发了一种便携式、低成本的数字显微镜扫描仪，能够进行明场和荧光成像。在此，我们使用该仪器对血涂片进行数字化处理，并应用深度学习（DL）算法检测恶性疟原虫寄生虫。
从坦桑尼亚农村地区显微镜确诊为恶性疟原虫感染的患者中，在开始基于青蒿素的联合治疗之前和之后采集薄血涂片（n = 125）。样本用4',6-二脒基-2-苯基吲哚荧光染料染色，并使用原型显微镜扫描仪进行数字化处理。训练了两种DL算法来检测样本中的疟原虫寄生虫，并将结果与数字化样本以及吉姆萨染色厚涂片的目视评估结果进行比较。
通过目视评估和基于DL的分析都可以在数字化薄血涂片中检测到恶性疟原虫寄生虫，结果具有很强的相关性（r = 0.99，p < 0.01）。基于DL的薄涂片分析与目视厚涂片分析之间观察到中等强度的相关性（r = 0.74，p < 0.01）。在开始治疗后的第三天，基于DL的分析检测到低水平的寄生虫，但在显微镜检查为阴性的样本中也检测到少量荧光信号。
使用DL支持的即时护理数字显微镜对DAPI染色的薄涂片中的恶性疟原虫寄生虫进行定量是可行的，与样本的目视评估具有高度相关性。低感染水平样本中假象产生的荧光信号是数字分析的主要挑战，因此凸显了尽量减少样本污染的重要性。所提出的方法可以通过自动定量寄生虫血症来支持疟疾诊断和治疗反应监测，并且可能也适用于其他疟原虫物种和其他传染病的诊断。
