Expert-level automated malaria diagnosis on routine blood films with deep neural networks
Expert-level automated malaria diagnosis on routine blood films with deep neural networks
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DOI：
10.1002/ajh.25827
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发表时间：
2020-04-30
影响因子：
12.8
通讯作者：
Fernandez-Reyes, Delmiro
中科院分区：
文献类型：
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作者：
Manescu, Petru;Shaw, Michael J.;Fernandez-Reyes, Delmiro
全球每年有2亿多疟疾病例导致50万人死亡。准确的疟疾诊断仍然是一项挑战。分析厚血膜（TBF）的自动化成像处理方法可以为撒哈拉以南疟疾全面流行地区的城市医疗保健提供者提供可扩展的解决方案。虽然已经尝试了几种方法来识别TBF中的疟疾寄生虫，但没有一种方法取得适合撒哈拉以南西部地区临床使用的阴性和阳性预测效果。虽然疟疾寄生虫目标检测仍然是实现患者自动诊断的中间步骤，但训练最先进的深度学习目标检测器需要人类专家的劳动密集型过程，即标记大型数字化TBF数据集。为了克服这些挑战并实现临床可用的系统，我们展示了一种新的方法。它利用来自我们质量控制的疟疾诊所的常规临床显微镜标签，训练深度疟疾卷积神经网络分类器（DeepMCNN）进行自动疟疾诊断。我们的系统还提供疟疾寄生虫（MP）总数和白细胞（WBC）计数，从而按照世界卫生组织的建议，以MP/ μ L估算寄生虫血症。DeepMCNN对专家级疟疾诊断的敏感性/特异性为0.92/0.90。我们的方法PPV/NPV性能为0.92/0.90，在伊巴丹人口稠密的大都市的全流行环境中可用于临床。它位于非洲人口最多的国家（尼日利亚），也是恶性疟原虫疟疾最严重的国家之一。我们公开提供的方法对于旨在扩大城市地区疟疾诊断规模的战略具有重要意义，因为城市地区每天需要对数千个标本进行评估。
Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban healthcare providers in the holoendemic malaria sub-Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub-Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state-of-the-art deep-learning object detectors requires the human-expert labor-intensive process of labeling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach. It leverages routine clinical-microscopy labels from our quality-controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/mu L, as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert-level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90, which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan. It is located within the most populous African country (Nigeria) and with one of the largest burdens of Plasmodium falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.