背景:显微镜检查通常用于该领域的疟疾诊断。然而,在受疾病影响最大的疟疾流行地区,缺乏训练有素的显微镜医师是一个严重的问题。此外,考试过程耗时且容易出现人为错误。基于机器学习的自动诊断系统为克服这些问题提供了巨大的潜力。本研究旨在评估疟疾筛查,基于智能手机的疟疾诊断应用程序。
方法:在喀土穆附近的两个农村地区招募了190名患者,苏丹。疟疾筛选器移动应用程序已部署到筛选Giemsa染色的血液涂片。进行专家显微镜和巢式PCR以用作参考标准。首先,使用两个参考标准对疟疾筛选器进行评估。然后,在研究后的实验中,对新开发的算法重复评估,疟原虫VF-Net。
结果:在阈值校准后,使用专家显微镜作为参考,疟疾筛选器检测恶性疟原虫疟疾的准确度达到74.1%(95%CI63.5-83.0)。与PCR相比,准确率达到71.8%(95%CI61.0-81.0)。所达到的准确度符合WHO对寄生虫检测的3级要求。每次涂片的处理时间从5到15分钟不等,取决于白细胞(WBC)的浓度。在研究后的实验中,当使用不同方法计算患者水平的结果时,疟疾筛选器达到91.8%(95%CI83.8-96.6)的准确性。该准确度符合WHO对寄生虫检测的1级要求。此外,疟原虫VF-Net,一种新开发的算法,与专家显微镜相比,准确率达到83.1%(95%CI77.0-88.1),与PCR相比准确率达到81.0%(95%CI74.6-86.3),达到WHO检测恶性疟原虫和间日疟原虫疟疾的2级要求,无需使用测试站点数据进行培训或校准。疟疾筛查和疟原虫VF-Net的报告结果均使用厚涂片进行诊断。在本文中,这两个系统都没有在物种识别和寄生虫计数方面进行评估,仍在开发中。
结论:疟疾筛查显示了在资源有限的地区部署以促进常规疟疾筛查的潜力。这是第一个基于智能手机的疟疾诊断系统,在自然野外环境中对患者水平进行评估。因此,此处报告的领域结果可作为未来研究的参考。
BACKGROUND: Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.
METHODS: A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net.
RESULTS: Malaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development.
CONCLUSIONS: Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.