Automated ABO Rh-D blood type detection using smartphone imaging for point-of-care medical diagnostics.

PAPER pubmed Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2016 Engineering / measurement Effect: unclear Evidence: Insufficient

Read original paper → DOI: 10.1109/embc.2016.7591689 PMID: 28269240 Evidence Lab

Abstract

We present a novel methodology for automated ABO Rh-D blood typing using simple morphological image processing algorithms to be used in conjunction with a fabric strip based rapid diagnostic test. Images of the fabric strip post testing are acquired using low cost mobile phones and the proposed algorithm proceeds to automatically identify the blood type by processing the images using steps comprising of noise reduction, range filtering and empirically derived heuristics. The ultimate goal is to provide a simple mobile phone application to enable automated, rapid and accessible blood type detection at the point-of-care.

AI evidence extraction

At a glance

Study type

Engineering / measurement

Effect direction

unclear

Population

—

Sample size

—

Exposure

mobile phone

Evidence strength

Insufficient

Confidence: 66% · Peer-reviewed: yes

Main findings

The paper presents an image-processing methodology that uses mobile phone-acquired images of a fabric strip rapid diagnostic test to automatically identify ABO Rh-D blood type using noise reduction, range filtering, and empirically derived heuristics.

Outcomes measured

Automated ABO Rh-D blood type detection from smartphone images of a fabric strip rapid diagnostic test

View raw extracted JSON

{
    "study_type": "engineering",
    "exposure": {
        "band": null,
        "source": "mobile phone",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": null,
    "sample_size": null,
    "outcomes": [
        "Automated ABO Rh-D blood type detection from smartphone images of a fabric strip rapid diagnostic test"
    ],
    "main_findings": "The paper presents an image-processing methodology that uses mobile phone-acquired images of a fabric strip rapid diagnostic test to automatically identify ABO Rh-D blood type using noise reduction, range filtering, and empirically derived heuristics.",
    "effect_direction": "unclear",
    "limitations": [],
    "evidence_strength": "insufficient",
    "confidence": 0.66000000000000003108624468950438313186168670654296875,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "ABO",
        "Rh-D",
        "blood typing",
        "smartphone imaging",
        "point-of-care",
        "image processing",
        "rapid diagnostic test",
        "fabric strip"
    ],
    "suggested_hubs": []
}

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AI-extracted fields are generated from the abstract/metadata and may be incomplete or incorrect. This content is for informational purposes only and is not medical advice.

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