Format

Send to

Choose Destination
See comment in PubMed Commons below
J Am Med Inform Assoc. 2016 Jan;23(1):166-73. doi: 10.1093/jamia/ocv015. Epub 2015 May 29.

Smartphone-based diagnostic for preeclampsia: an mHealth solution for administering the Congo Red Dot (CRD) test in settings with limited resources.

Author information

1
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520, USA Department of Medical Informatics, RWTH Aachen University, Aachen, Germany sjonas@mi.rwth-aachen.de.
2
Department of Medical Informatics, RWTH Aachen University, Aachen, Germany.
3
Department of Obstetrics & Gynecology, The Ohio State University College of Medicine, Columbus, OH 43210, USA Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43215, USA.
4
Department of Obstetrics & Gynecology, University of Pretoria, Kalafong Hospital, South Africa.
5
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520, USA Department of Biomedical Engineering, Yale University School of Engineering and Applied Science, New Haven, CT 06520, USA Department of Pediatrics, Yale University School of Medicine, New Haven, CT 06520, USA.
6
Department of Obstetrics & Gynecology, The Ohio State University College of Medicine, Columbus, OH 43210, USA Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43215, USA Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43215, USA.

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 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).

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.

KEYWORDS:

Congo-red; global health; high-throughput; low-resource; mHealth; preeclampsia

PMID:
26026158
DOI:
10.1093/jamia/ocv015
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center