Pollak Joseph Joel, Houri-Yafin Arnon, Salpeter Seth J
Sight Diagnostics Ltd., Jerusalem, Israel.
Front Public Health. 2017 Aug 21;5:219. doi: 10.3389/fpubh.2017.00219. eCollection 2017.
Accurate malaria diagnosis is critical to prevent malaria fatalities, curb overuse of antimalarial drugs, and promote appropriate management of other causes of fever. While several diagnostic tests exist, the need for a rapid and highly accurate malaria assay remains. Microscopy and rapid diagnostic tests are the main diagnostic modalities available, yet they can demonstrate poor performance and accuracy. Automated microscopy platforms have the potential to significantly improve and standardize malaria diagnosis. Based on image recognition and machine learning algorithms, these systems maintain the benefits of light microscopy and provide improvements such as quicker scanning time, greater scanning area, and increased consistency brought by automation. While these applications have been in development for over a decade, recently several commercial platforms have emerged. In this review, we discuss the most advanced computer vision malaria diagnostic technologies and investigate several of their features which are central to field use. Additionally, we discuss the technological and policy barriers to implementing these technologies in low-resource settings world-wide.
准确的疟疾诊断对于预防疟疾死亡、遏制抗疟药物的过度使用以及促进对其他发热原因的适当管理至关重要。虽然存在多种诊断测试，但仍需要一种快速且高度准确的疟疾检测方法。显微镜检查和快速诊断测试是现有的主要诊断方式，但它们的性能和准确性可能较差。自动化显微镜平台有潜力显著改善和标准化疟疾诊断。基于图像识别和机器学习算法，这些系统保留了光学显微镜的优点，并提供了诸如更快的扫描时间、更大的扫描面积以及自动化带来的更高一致性等改进。虽然这些应用已经开发了十多年，但最近出现了几个商业平台。在本综述中，我们讨论了最先进的计算机视觉疟疾诊断技术，并研究了它们在现场使用中的几个核心特征。此外，我们还讨论了在全球资源匮乏地区实施这些技术的技术和政策障碍。
