Fig 3 values.

BackgroundUrogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis.MethodologyDigital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample.ResultsA total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database.ConclusionsThe development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.背景 泌尿生殖道血吸虫病被世界卫生组织（World Health Organization, WHO）列为被忽视的热带病（Neglected Tropical Disease, NTD）。据估计，全球共有1.5亿人受该疾病困扰，在非洲大陆资源匮乏地区的公共卫生相关性尤为突出。当前的金标准诊断方法仍为通过光学显微镜直接观察尿液样本中的埃及血吸虫（Schistosoma haematobium）虫卵。基于人工智能（Artificial Intelligence, AI）工具的数字图像分析新型诊断技术，是血吸虫病诊断的可行替代方案。 方法 本研究在非流行地区获取了24份尿沉渣样本的数字图像。实验室专业人员对图像中的埃及血吸虫虫卵进行人工标注，用于训练YOLOv5与YOLOv8模型，以实现虫卵的自动检测与定位。此外，研究还利用尿沉渣图像，结合MobileNetv3Large、EfficientNetv2及NasNetLarge模型，完成红细胞与白细胞的二分类检测。本研究采用自动化显微镜系统，可自动控制载玻片沿X-Y轴移动并完成样本自动对焦。 结果 共计在1017张尿沉渣样本数字图像中完成1189个标注。针对埃及血吸虫虫卵检测任务，YOLOv5x模型训练后获得了99.3%的精确率、99.4%的召回率、99.3%的F1值及99.4%的mAP0.5。针对红细胞/白细胞检测任务，NasNetLarge模型在测试集上的准确率达85.6%。卷积神经网络训练与对比分析显示，用于虫卵检测的YOLOv5x与用于红细胞/白细胞二分类的NasNetLarge为本研究数字图像数据库中的最优模型选型。 结论 基于AI工具实现尿液中埃及血吸虫虫卵检测与识别的低成本新型诊断技术，可作为非流行地区传统显微镜检查的可行替代方案。本项原理性技术验证研究为系统优化及实验室落地应用奠定了基础。