Name
Title | ||
|---|---|---|
Smartphone-based diagnostic for preeclampsia: an mHealth solution for administering the Congo Red Dot (CRD) test in settings with limited resources. |
Abstract | ||
|---|---|---|
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 pre-eclampsia. 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 rho(c) = 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 rho(c) = 0.914). Conclusion 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/jamia/ocv015 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
preeclampsia,mHealth,global health,Congo-red,low-resource,high-throughput | Robust analysis,Data mining,Image processing,Concordance,Robustness (computer science),mHealth,Digital image processing,Medicine | Journal |
Volume | Issue | ISSN |
23 | 1 | 1067-5027 |
Citations | PageRank | References |
3 | 0.43 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephan Jonas | 1 | 35 | 8.92 |
| Thomas M Deserno | 2 | 358 | 33.79 |
| catalin s buhimschi | 3 | 3 | 0.43 |
| j d makin | 4 | 3 | 0.43 |
| Michael A. Choma | 5 | 3 | 0.77 |
| irina a buhimschi | 6 | 3 | 0.43 |