通过使用深 convolutional 神经网络与微观聚合试验图像来增强勒螺杆菌病查
Murnihayati Hassan1, Siti Nur Zawani Rosli1, Natasya Amirah Mohamed Tahir1
1Bacteriology Unit, Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health (MOH), 40170 Setia Alam, Shah Alam, Selangor, Malaysia.
Biology methods & protocols
|June 26, 2025
概括
这项研究开发了一种半自动化的工作流程,通过将人工智能与微观聚凝试验 (MAT) 整合起来,用于Leptospira查. 这种混合方法提高了马来西亚螺杆菌病的诊断速度和准确性.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 瘦肉是一种重要的全球公共卫生问题,特别是在马来西亚流行,季节性高峰.
- 微观凝结试验 (MAT) 是验证白病的标准,但是劳动密集型和主观的.
- 目前用于白病的诊断方法在效率和准确性方面存在挑战.
研究的目的:
- 开发一个半自动化工作流程来选Leptospira.
- 将一个深度卷积神经网络 (DCNN) 与传统的 MAT 集成.
- 为了提高勒螺杆菌病诊断的速度和减少不准确性.
主要方法:
- 开发一种混合诊断工作流程,将TensorFlow和基于Keras的DCNN与MAT结合起来.
- 训练DCNN模型在442个阳性和442个负性的马来西亚LeptoSpira血清病毒的MAT图像的数据集上.
- DCNN模型的超参数调整,包括卷积层,过器,内核大小,密集层单元,激活函数和学习率.
主要成果:
- 与经过验证的患者MAT结果相比,开发的模型获得了0.8125的精度得分,0.9286的回忆和0.8667的F1-Score.
- 与传统方法相比,半自动化工作流显示出速度和准确性的显著改善.
- 该模型的性能表明其在莱普托斯匹拉诊断中的实际应用潜力.
结论:
- 混合诊断方法为马来西亚的白病管理提供了重大进展.
- 开发的DCNN集成的MAT工作流程是实用的,可适应的,适合其他实验室诊断Leptospira.
- 进一步扩展数据集可以提高模型的准确性和适应性,以便在其他特有地区使用.
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