利用显微镜凝集试验图像的深度卷积神经网络增强钩端螺旋体病筛查。

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS
Biology Methods and Protocols Pub Date : 2025-06-09 eCollection Date: 2025-01-01 DOI:10.1093/biomethods/bpaf047
Murnihayati Hassan, Siti Nur Zawani Rosli, Natasya Amirah Mohamed Tahir, Nurul Azmawati Mohamed, Khairunnisa Mohd Sukri, Liyana Azmi, Norhasmira Mohammad
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引用次数: 0

摘要

钩端螺旋体病对全球公共卫生构成重大挑战。在马来西亚,钩端螺旋体病是地方性疾病,每年的病例在季风季节达到高峰。显微镜凝集试验(MAT)是确认钩端螺旋体病的金标准血清学方法。然而,它是劳动密集型和耗时的,因为它依赖于医学实验室技术人员的主观解释。本研究通过将TensorFlow和定制设计的基于keras的深度卷积神经网络(DCNN)与传统MAT集成,描述了钩端螺旋体筛选的半自动化工作流程的开发。我们使用了442张阳性和442张阴性MAT图像的数据集,其中包括来自马来西亚的钩端螺旋体血清型的混合物来训练模型。该模型进行了超参数调整,调整了卷积层数、滤波器、核大小、密集层中的单元、激活函数和学习率。将我们测试的模型与经过验证的患者MAT结果进行验证,获得以下指标:Precision得分为0.8125,Recall得分为0.9286,f1得分为0.8667。将我们的模型与当前马来西亚钩端螺旋体工作流程相结合,可以显著加快,减少不准确性,并改善钩端螺旋体病的管理。此外,该模型的应用具有实用性和适应性,适用于其他实验室观察MAT作为钩端螺旋体诊断。据我们所知,该方法是马来西亚首个钩端螺旋体混合诊断方法。扩大数据集将提高模型的准确性,使其适用于其他钩端螺旋体病流行的地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing leptospirosis screening using a deep convolutional neural network with microscopic agglutination test images.

Leptospirosis poses substantial challenges to global public health. In Malaysia, leptospirosis is endemic, with annual cases peaking during the monsoon season. The microscopic agglutination test (MAT) is the gold-standard serological method for confirmation of leptospirosis. However, it is labor-intensive and time-consuming, as it relies on the subjective interpretation of medical lab technicians. This study describes the development of a semiautomated workflow for Leptospira screening by integrating a TensorFlow and custom-designed Keras-based Deep Convolutional Neural Network (DCNN) with conventional MAT. We used a dataset of 442 positive and 442 negative MAT images, which consisted of a mixture of Leptospira serovars from Malaysia to train the model. The model was subjected to hyperparameter tuning, which modulated the number of convolutional layers, filters, kernel sizes, units in dense layers, activation functions, and learning rate. Verification of our tested model compared to the verified patient MAT results achieved the following metrics: a Precision score of 0.8125, a Recall of 0.9286, and an F1-Score of 0.8667. Combining our model with the current Malaysia Leptospira workflow can significantly speed up, reduce inaccuracies, and improve the management of leptospirosis. Furthermore, the application of this model is practical and adaptable, making it suitable for other labs that observe MAT as their Leptospira diagnosis. To our knowledge, this approach is Malaysia's first hybrid diagnostic approach for Leptospira diagnosis. Scaling up the dataset would enhance the model's accuracy, making it adaptable in other regions where leptospirosis is endemic.

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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
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