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Allergic Reactions02:06

Allergic Reactions

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Allergy Wheal and Erythema Segmentation Using Attention U-Net.

Yul Hee Lee1, Ji-Su Shim2, Young Jae Kim3

  • 1Department of Nursing, Gachon University College of Nursing, 191, Hambangmoe-ro, Yeonsu-gu, Incheon, 21936, Korea.

Journal of Imaging Informatics in Medicine
|August 9, 2024
PubMed
Summary

This study introduces a deep learning model to automatically measure skin prick test (SPT) reactions, improving allergy diagnosis. The AI accurately segments wheals and erythema, streamlining the allergy testing process.

Keywords:
Deep learningErythemaSegmentationSkin Prick TestWheal

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Area of Science:

  • Allergy and Immunology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • The skin prick test (SPT) is crucial for diagnosing IgE-mediated allergic diseases.
  • Current SPT methods are time-consuming and labor-intensive due to manual measurement of skin reactions.
  • Objective quantification of SPT results is needed to improve diagnostic consistency.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated segmentation of wheals and erythema in SPT images.
  • To assess the performance of image preprocessing techniques in enhancing SPT image analysis.
  • To determine the feasibility of using smartphone-captured images for objective SPT evaluation.

Main Methods:

  • Employed contrast-limited adaptive histogram equalization (CLAHE) for image preprocessing.
  • Developed deep learning models for segmenting wheals and erythema from smartphone-captured SPT images.
  • Validated model performance against ground-truth data, calculating accuracy, sensitivity, specificity, and Dice similarity coefficient.

Main Results:

  • The wheal segmentation model achieved high accuracy (0.9985) and specificity (0.9995).
  • The erythema segmentation model demonstrated strong performance with an accuracy of 0.9660 and specificity of 0.97977.
  • Both models showed moderate sensitivity (wheal: 0.5621, erythema: 0.5787) and Dice coefficients (wheal: 0.7079, erythema: 0.6636).

Conclusions:

  • Image preprocessing combined with deep learning offers a promising approach for accurate and consistent SPT analysis.
  • This technology has the potential to simplify allergy diagnosis and improve medical practice.
  • Automated segmentation of SPT reactions can lead to more objective and efficient patient evaluations.