Davis Anne M, Tomitaka Asahi
Department of Computer and Information Sciences, University of Houston-Victoria, Victoria, TX 77904, USA.
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA.
Biosensors (Basel). 2025 Jan 4;15(1):19. doi: 10.3390/bios15010019.
Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images.
侧向流动分析已广泛用于家庭检测以及农村地区的即时诊断。尽管它具有方便且低成本检测的优点，但它的定量能力较差，只能实现是/否或阳性/阴性诊断。在本研究中，开发了机器学习和深度学习模型，以从智能手机拍摄的侧向流动分析测试图像中量化分析物负载。对比分析表明，与其他成熟的机器学习模型相比，随机森林和卷积神经网络（CNN）模型在对侧向流动分析结果进行分类方面表现良好。在小尺寸图像上进行训练时，随机森林模型在图像分类方面优于CNN模型。相反，在对噪声图像进行分类时，CNN模型优于随机森林模型。
