Sánchez-Montejo Javier, Marín Miguel, Villamizar-Monsalve María Alejandra, Vieira María Del Carmen, Vicente Belén, Peláez Rafael, López-Abán Julio, Muro Antonio
Infectious and Tropical Diseases Research Group (E-INTRO), Biomedical Research Institute of Salamanca-Research Centre for Tropical Diseases at the University of Salamanca (IBSAL-CIETUS), Faculty of Pharmacy, University of Salamanca, 37007, Salamanca, Spain.
Department of Pharmaceutical Sciences, Biomedical Research Institute of Salamanca-Research Centre for Tropical Diseases at the University of Salamanca (IBSAL-CIETUS), Faculty of Pharmacy, University of Salamanca, 37007, Salamanca, Spain.
Parasit Vectors. 2025 Feb 2;18(1):36. doi: 10.1186/s13071-025-06664-8.
BACKGROUND-OBJECTIVE: Trichinella spiralis drug development and control need an objective high throughput system to assess first stage larvae (L1) viability. YOLOv5 is an image recognition tool easily trained to count muscular first stage larvae (L1) and recognize morphological differences. Here we developed a semi-automated system based on YOLOv5 to capture photographs of 96 well microplates and use them for L1 count and morphological damage evaluation after experimental drug treatments.
Morphological properties were used to distinguish L1 from debris after pepsin muscle digestion and distinguish healthy (serpentine) or damaged (coiled) L1s after 72 h untreated or treated with albendazole or mebendazole cultures. An AxiDraw robotic arm with a smartphone was used to scan 96 well microplates and store photographs. Images of L1 were manually annotated, and augmented based on exposure, bounding, blur, noise, and mosaicism.
A total of 1309 photographs were obtained that after L1 labeling and data augmentation gave 27478 images. The final dataset of 12571 healthy and 14907 affected L1s was used for training, testing, and validating in a ratio of 70/20/10 respectively. A correlation of 92% was found in a blinded comparison with bare-eye assessment by experienced technicians.
YOLOv5 is capable of accurately counting and distinguishing between healthy and affected L1s, thus improving the performance of the assessment of meat inspection and potential new drugs.
背景 - 目的：旋毛虫药物研发与控制需要一个客观的高通量系统来评估第一期幼虫（L1）的活力。YOLOv5是一种图像识别工具，易于训练以对肌肉中的第一期幼虫（L1）进行计数并识别形态差异。在此，我们开发了一种基于YOLOv5的半自动系统，用于拍摄96孔微孔板的照片，并在实验性药物处理后用于L1计数和形态损伤评估。
利用形态学特性在胃蛋白酶消化肌肉后将L1与碎片区分开来，并在未经处理或用阿苯达唑或甲苯达唑培养72小时后区分健康（蜿蜒状）或受损（盘绕状）的L1。使用配备智能手机的AxiDraw机器人手臂扫描96孔微孔板并存储照片。L1的图像进行了手动标注，并根据曝光、边界、模糊、噪声和镶嵌性进行了增强。
共获得1309张照片，在对L1进行标记和数据增强后得到27478张图像。最终数据集包含12571个健康L1和14907个受影响的L1，分别以70/20/10的比例用于训练、测试和验证。在与经验丰富的技术人员进行的盲法肉眼评估比较中，发现相关性为92%。
YOLOv5能够准确计数并区分健康和受影响的L1，从而提高肉类检验和潜在新药评估的性能。
