Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
PLoS Negl Trop Dis. 2024 Nov 5;18(11):e0012614. doi: 10.1371/journal.pntd.0012614. eCollection 2024 Nov.
Urogenital 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.
Digital 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.
A 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.
The 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.
尿路血吸虫病被世界卫生组织（WHO）视为被忽视的热带病（NTD）。据估计，全世界有 1.5 亿人受到影响，在非洲大陆资源匮乏的环境中相关性很高。金标准诊断仍然是通过光学显微镜直接观察尿液样本中的曼氏血吸虫卵。基于人工智能（AI）工具的数字图像分析的新型诊断技术是血吸虫病诊断的合适替代方法。
在非流行地区采集了 24 个尿液沉淀物样本的数字图像。实验室专业人员在数字图像中手动标记曼氏血吸虫卵，并用于训练 YOLOv5 和 YOLOv8 模型，该模型将实现对卵的自动检测和定位。还使用尿液沉淀物图像对图像进行二进制分类，使用 MobileNetv3Large、EfficientNetv2 和 NasNetLarge 模型检测红细胞/白细胞。机器人显微镜系统用于自动沿 X-Y 轴移动载玻片并自动聚焦样本。
在 1017 张来自尿液沉淀物样本的数字图像中总共标记了 1189 个标签。YOLOv5x 训练对曼氏血吸虫的检测显示出 99.3%的精度、99.4%的召回率、99.3%的 F 分数和 99.4%的 mAP0.5。NasNetLarge 在测试数据集上对红细胞/白细胞的检测准确率为 85.6%。卷积神经网络的训练和比较表明，YOLOv5x 用于检测卵，NasNetLarge 用于对红细胞/白细胞进行二进制图像分类，这是我们数字图像数据库的最佳选择。
基于 AI 工具在尿液中检测和识别曼氏血吸虫卵的低成本新型诊断技术的开发将是在非流行地区替代传统显微镜的合适选择。这项技术原理验证研究为改进系统奠定了基础，并优化了其在实验室中的实施。
