Research Institute Hospital 12 de Octubre, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain.
SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain.
Malar J. 2018 Jan 30;17(1):54. doi: 10.1186/s12936-018-2194-8.
Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. In addition, differential diagnosis of Plasmodium species has a high level of misdiagnosis. Real time remote microscopical diagnosis through on-line crowdsourcing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification-a critical step during the diagnosis protocol in order to choose the appropriate medication-is possible through the information provided by non-trained on-line volunteers.
88 volunteers have performed a series of questionnaires over 110 images to differentiate species (Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, Plasmodium knowlesi) and parasite staging from thin blood smear images digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Visual cues evaluated in the surveys include texture and colour, parasite shape and red blood size.
On-line volunteers are able to discriminate Plasmodium species (P. falciparum, P. malariae, P. vivax, P. ovale, P. knowlesi) and stages in thin-blood smears according to visual cues observed on digitalized images of parasitized red blood cells. Friendly textual descriptions of the visual cues and specialized malaria terminology is key for volunteers learning and efficiency.
On-line volunteers with short-training are able to differentiate malaria parasite species and parasite stages from digitalized thin smears based on simple visual cues (shape, size, texture and colour). While the accuracy of a single on-line expert is far from perfect, a single parasite classification obtained by combining the opinions of multiple on-line volunteers over the same smear, could improve accuracy and reliability of Plasmodium species identification in remote malaria diagnosis.
由于缺乏人员、培训和样本处理能力，农村和资源匮乏地区的疟疾常规现场诊断是一项巨大的挑战。此外，疟原虫种的鉴别诊断误诊率很高。通过在线众包平台进行实时远程显微镜诊断，可以将其转化为一个灵活的网络，支持资源匮乏地区的基于诊断的治疗和疟疾控制。本研究探讨了通过非训练有素的在线志愿者提供的信息，是否可以实现准确的疟原虫种鉴定-这是诊断方案中的关键步骤，以便选择合适的药物。
88 名志愿者通过智能手机摄像头拍摄的数字化薄血涂片图像，对 110 多张图像进行了一系列问卷调查，以区分物种（恶性疟原虫、卵形疟原虫、间日疟原虫、三日疟原虫、诺氏疟原虫）和寄生虫分期。在调查中评估的视觉线索包括纹理和颜色、寄生虫形状和红细胞大小。
在线志愿者能够根据数字化寄生虫红细胞图像上观察到的视觉线索，区分薄血涂片上的疟原虫种（恶性疟原虫、三日疟原虫、间日疟原虫、卵形疟原虫、诺氏疟原虫）和分期。对视觉线索的友好文字描述和专门的疟疾术语是志愿者学习和效率的关键。
经过短期培训的在线志愿者能够根据简单的视觉线索（形状、大小、纹理和颜色）区分数字化薄血涂片上的疟原虫种和寄生虫分期。虽然单个在线专家的准确性远非完美，但通过对同一张涂片的多个在线志愿者的意见进行组合，可以提高远程疟疾诊断中疟原虫种鉴定的准确性和可靠性。
