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Dried Blood Spots - Preparing and Processing for Use in Immunoassays and in Molecular Techniques

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Published on: March 13, 2015

Automatic disease screening method using image processing for dried blood microfluidic drop stain pattern

Basant S Sikarwar¹, Mukesh Roy¹, Priya Ranjan²

¹a Department of ME , Amity University Uttar Pradesh , Noida , India ;

Journal of Medical Engineering & Technology

|April 22, 2016

Summary

This summary is machine-generated.

This study introduces a low-cost, non-invasive method for automatic infection detection using dried blood stain patterns. The technique successfully distinguishes between healthy individuals and those with tuberculosis or anemia based on unique stain formations.

Keywords:

Pattern recognition dried blood microdrop stain pattern microfluidic device physical properties of blood tuberculosis

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Area of Science:

Biomedical Engineering
Medical Diagnostics
Pattern Recognition

Background:

Traditional blood tests can be invasive, costly, and require specialized laboratory equipment.
Patient health status, such as infections and disease, alters blood's physical properties.
These alterations can manifest in the patterns formed by dried micro-scale blood drop stains.

Purpose of the Study:

To develop and validate a programmed, automatic method for recognizing infections from dried blood stain patterns.
To establish a low-cost, less-intrusive diagnostic alternative, particularly beneficial for infants and the elderly.
To differentiate between healthy individuals and patients with specific diseases like tuberculosis and anemia using blood stain analysis.

Main Methods:

Utilized programmed automatic examination of dried micro-scale blood drop stain designs.
Employed an algorithm based on pattern recognition to analyze stain morphology.
Investigated the fundamental flow mechanics influencing dried blood stain formation.

Main Results:

Distinct distinguishing features were identified between blood stains from healthy and infected individuals.
Dried blood stains from non-ailing individuals exhibited a characteristic thick ring-like feature at the periphery.
Patients with anemia or tuberculosis showed thin, line-like features in their dried blood stains, contrasting with healthy controls.

Conclusions:

Programmed automatic recognition of infection from dried blood stain patterns is feasible and effective.
The method offers a low-cost, non-invasive diagnostic approach for conditions like tuberculosis and anemia.
Analysis of dried blood stain morphology, driven by flow mechanics, provides valuable diagnostic information.