A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning

2020 · Harm · Very low evidence

← Research stream

HarmVery low evidencePaper 1574

Microscopic segmented brain images of Drosophila under EMF exposure showed geometrical features with discriminatory properties that enabled machine-learning classifiers to distinguish exposed vs non-exposed brains, with reported classification performance up …

Study snapshot

Publicationmanual · 2020

Study typeanimal

Reported directionHarm

Evidence ratingVery low

Abstract

A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning Singh A, Singh N, Jinda T, Rosado-Muñoz, A, Kishore Dutta M. A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning. Biomedical Signal Processing and Control. Vol. 57, March 2020, 101821. doi.org. Highlights • This work presents automated identification of the effect of EMF radiations on brain. • Changes in brain morphology due to EMF exposure were analyzed considering drosophila melanogaster as a specimen. • The geometrical features were extracted from the microscopic segmented brain image of drosophila. • Machine learning techniques were used for identification of EMF exposure on drosophila brain. Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometrical features are extracted to identify the effect of EMF exposure. The geometrical features of the microscopic segmented brain image of drosophila are analyzed and found to have discriminatory properties suitable for machine learning. The most prominent discriminatory features were fed to four different classifiers: support vector machine, naïve bayes, artificial neural network and random forest for classification of exposed / non-exposed microscopic image of drosophila brain. Experimental results indicate that all four classifiers provide good classification results up to 94.66% using discriminatory features selected by feature selection method. The proposed method is a novel approach to identify the effect of EMF exposure automatically and with low time complexity thus providing an efficient image processing framework based on machine learning. sciencedirect.com ence/article/abs/pii/S1746809419304021

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 typeanimal

Effect directionharm

Populationdrosophila melanogaster

Sample sizeNot supplied

ExposureNot supplied

Evidence strengthVery lowConfidence: 66% · Peer reviewed: yes

Main findings

Outcomes measured

Brain morphology changes (microscopic image-derived geometrical features)
Classification accuracy for exposed vs non-exposed brains using machine learning (SVM, naïve Bayes, ANN, random forest)

Limitations

Pilot study (as described)
No exposure parameters reported (e.g., frequency, SAR, duration)
Sample size not reported
Outcome is indirect (image-feature discrimination/classification) rather than direct biological/clinical endpoints

View raw extracted JSON

{
  "study_type": "animal",
  "exposure": {
    "band": null,
    "source": "mobile phone and cell tower",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": "drosophila melanogaster",
  "sample_size": null,
  "outcomes": [
    "Brain morphology changes (microscopic image-derived geometrical features)",
    "Classification accuracy for exposed vs non-exposed brains using machine learning (SVM, naïve Bayes, ANN, random forest)"
  ],
  "main_findings": "Microscopic segmented brain images of Drosophila under EMF exposure showed geometrical features with discriminatory properties that enabled machine-learning classifiers to distinguish exposed vs non-exposed brains, with reported classification performance up to 94.66% using selected features.",
  "effect_direction": "harm",
  "limitations": [
    "Pilot study (as described)",
    "No exposure parameters reported (e.g., frequency, SAR, duration)",
    "Sample size not reported",
    "Outcome is indirect (image-feature discrimination/classification) rather than direct biological/clinical endpoints"
  ],
  "evidence_strength": "very_low",
  "confidence": 0.66,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "electromagnetic field",
    "EMF",
    "mobile phone",
    "cell tower",
    "drosophila melanogaster",
    "brain morphology",
    "microscopy",
    "image segmentation",
    "computer vision",
    "machine learning",
    "support vector machine",
    "naïve bayes",
    "artificial neural network",
    "random forest"
  ],
  "suggested_hubs": [
    {
      "slug": "animal-studies",
      "weight": 0.86,
      "reason": "Uses Drosophila melanogaster to assess brain morphology under EMF exposure."
    }
  ]
}

AI can be wrong. Always verify against the paper.

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: 10.1016/j.bspc.2019.101821
PubMed: Not supplied

Read the original paper →

Related papers

← Back to the Research Library