疟疾是撒哈拉以南非洲最普遍的传染病之一,根据世界卫生组织的数据,2021年全球报告了2.47亿病例。光学显微镜仍然是疟疾诊断的黄金标准技术,然而,它需要专业知识,既耗时又难以复制。因此,基于使用人工智能工具的数字图像分析的新诊断技术可以改善诊断并帮助自动化诊断。
■在这项研究中,创建了2571张标记的厚血涂片图像的数据集.YOLOv5x,更快的R-CNN,SSD,和RetinaNet对象检测神经网络在相同的数据集上进行训练,以评估它们在疟原虫寄生虫检测中的性能。应用注意力模块并与YOLOv5x结果进行比较。要使整个诊断过程自动化,3D打印件的原型被设计用于传统光学显微镜的机器人化,能够自动聚焦样品和跟踪整个幻灯片。
■比较分析得出YOLOv5x在精度为92.10%的测试集上的性能,93.50%召回,92.79%F分数,和94.40%mAP0.5的白细胞,早期和成熟的疟原虫滋养体整体检测。白细胞各分类的F-得分值为99.0%,早期滋养体检测为88.6%,成熟滋养体检测为87.3%。与YOLOv5x原始训练模型相比,注意力模块的性能表现出无显着的统计差异。预测模型被集成到智能手机计算机应用程序中,用于实验室中基于图像的诊断。该系统可以通过机器人显微镜的自动对焦和X-Y运动来执行全自动诊断,为数字图像分析而训练的CNN模型,和智能手机设备。新的原型将确定Giemsa染色的厚血涂片样本对疟原虫感染及其寄生虫水平是否为阳性/阴性。整个系统已集成到iMAGING智能手机应用程序中。
通过自动对焦和滑动运动实现全自动系统的合并,并通过智能手机软件和AI算法自动检测数字图像中的疟原虫寄生虫,使原型具有最佳功能,可以加入全球抗击疟疾的努力。被忽视的热带病和其他传染病。
UNASSIGNED: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.
UNASSIGNED: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide.
UNASSIGNED: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application.
UNASSIGNED: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.