目的:在资源有限的情况下,先兆子痫的发病率和死亡率通常由延迟诊断引起。刚果红点(CRD)测试,一种评估尿液中错误折叠蛋白质存在的简单方法,显示有望作为先兆子痫的诊断和预后工具。我们提出了一种创新的移动健康(mHealth)解决方案,可以将CRD测试量化为批量实验室测试,以最小的成本和设备。
方法:一个智能手机应用程序,可指导用户完成七个简单步骤,非专业人员可以成功使用,已开发。图像采集后,在智能手机上进行强大的分析,量化CRD测试响应,而不需要互联网连接或额外的硬件。在第一阶段,使用来自218例患者的尿液样本开发了基本的图像处理算法和辅助测试标准化。在第二阶段,对来自273名女性的328份尿液样本进行了标准化程序评估.第三阶段,使用4个不同的操作者和94个改变的样本对该应用的稳健性进行了测试.
结果:在第一阶段,图像处理链的建立与人工分析高度相关(z检验P<0.001).在第二阶段,计算出手动处理和自动处理之间的高度一致性(Lin的一致性系数ρc=0.968)。在最后阶段,确定了错误的来源,并相应地制定了补救措施。改变样品导致与手动黄金标准的可接受一致性(Lin'sρc=0.914)。
结论:将基于智能手机的图像分析与分子特异性疾病特征相结合,代表了mHealth的一种具有成本效益的应用,有可能填补获得医疗保健解决方案的空白,这些解决方案对于减少资源匮乏地区的不良事件至关重要。
OBJECTIVE: Morbidity and mortality due to preeclampsia in settings with limited resources often results from delayed diagnosis. The Congo Red Dot (CRD) test, a simple modality to assess the presence of misfolded proteins in urine, shows promise as a diagnostic and prognostic tool for preeclampsia. We propose an innovative mobile health (mHealth) solution that enables the quantification of the CRD test as a batch laboratory test, with minimal cost and equipment.
METHODS: A smartphone application that guides the user through seven easy steps, and that can be used successfully by non-specialized personnel, was developed. After image acquisition, a robust analysis runs on a smartphone, quantifying the CRD test response without the need for an internet connection or additional hardware. In the first stage, the basic image processing algorithms and supporting test standardizations were developed using urine samples from 218 patients. In the second stage, the standardized procedure was evaluated on 328 urine specimens from 273 women. In the third stage, the application was tested for robustness using four different operators and 94 altered samples.
RESULTS: In the first stage, the image processing chain was set up with high correlation to manual analysis (z-test P < 0.001). In the second stage, a high agreement between manual and automated processing was calculated (Lin\'s concordance coefficient ρc = 0.968). In the last stage, sources of error were identified and remedies were developed accordingly. Altered samples resulted in an acceptable concordance with the manual gold-standard (Lin\'s ρc = 0.914).
CONCLUSIONS: Combining smartphone-based image analysis with molecular-specific disease features represents a cost-effective application of mHealth that has the potential to fill gaps in access to health care solutions that are critical to reducing adverse events in resource-poor settings.