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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016 · Unclear · Insufficient evidence

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UnclearInsufficient evidencePaper 2887

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.

Study snapshot

PublicationAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016

Study typeengineering

Reported directionUnclear

Evidence ratingInsufficient

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.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeengineering

Effect directionunclear

PopulationNot supplied

Sample sizeNot supplied

ExposureNot supplied

Evidence strengthInsufficientConfidence: 66% · Peer reviewed: yes

Main findings

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": 0.66,
  "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 and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1109/embc.2016.7591689
PubMed: 28269240

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