Tackling Malaria Diagnosis in sub-Saharan Africa with Fast, Accurate and Scalable Robotic Automation, Computer Vision and Machine Learning (FASt-Mal)

疟疾影响全世界约3亿人，每年导致约100万人死亡。高达85%的病例发生在撒哈拉以南非洲，由于严重的疟疾综合征，五岁以下儿童的死亡率约为90%。控制疟疾仍然是撒哈拉以南非洲发展中国家的一个重大公共卫生问题。全球四分之一的疟疾病例和三分之一的可归因于疟疾的儿童死亡发生在非洲人口最多的国家尼日利亚（1.6亿居民），这表明了该问题的重要性。准确的疟疾诊断依赖于对临床参数的识别，更重要的是在显微镜下检测疟疾寄生虫，即外周血膜中被寄生的红细胞。通过人工操作的光学显微镜检测和计数疟原虫是目前的“黄金标准”，尽管存在重大的严重缺陷，但其他非显微镜方法的效果一直无法超过它。（未经显微镜证实）会浪费药物，如果诊断错误则无效，通常会消耗宝贵的卫生资源，助长了抗疟抗药性，使控制和消灭疟疾的干预措施无法实现。我们的目标是在现实世界的条件下创建和测试一个快速，准确和可扩展的疟疾诊断系统，用机器人自动化计算机专家系统FASt-MalPrototype代替人类专家光学显微镜，评估类似的数字光学显微镜表示的问题。该系统旨在提供有效的疟疾诊断服务，这是疟疾流行的所有发展中国家都面临的挑战。

Malaria affect about 300 million people worldwide leading to around one million deaths each year. Up to eighty-five percent of the cases occur in sub-Saharan Africa with about 90% mortality in the under five years-of-age group due to severe malaria syndromes. Control of malaria remains a major public health issue in sub-Saharan Africa developing countries. A quarter of the global malaria cases and a third of malaria-attributable childhood deaths occur in the most populous country of Africa, Nigeria (160M inhabitants) and indicates the importance of the problem. Accurate malaria diagnosis relies on the recognition of clinical parameters and more importantly in the microscopic detection of malarial parasites, parasitised red-blood-cells in peripheral-blood films. Malaria parasite detection and counting by human-operated optical microscopy is the current "gold standard" and despite its major severe drawbacks, other non-microscopic methodologies have not been able to outperform it. Presumptive treatment for malaria (without microscopic confirmation) is wasteful of drugs and ineffective if the diagnosis was wrong, a drain on often precious health resources, fuels antimalarial resistance and have made control and elimination interventions unachievable. We aim to create and test in real-world conditions a fast, accurate and scalable malaria diagnosis system by replacing human-expert optical-microscopy with a robotic automated computer-expert system FASt-MalPrototype that assesses similar digital-optical-microscopy representations of the problem. The system aims to provide access to effective malaria diagnosis, a challenge that is faced by all developing countries where malaria is endemic.