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Background: The skin prick test (SPT) is the gold standard for diagnosing sensitization to inhalant allergies. The Skin Prick Automated Test (SPAT) device was designed for increased consistency in test results, and captures 32 images to be jointly used for allergy wheal detection and delineation, which leads to a diagnosis. Materials and Methods: Using SPAT data from 868 patients with suspected inhalant allergies, we designed an automated method to detect and delineate wheals on these images. To this end, 10, 416 wheals were manually annotated by drawing detailed polygons along the edges. The unique data-modality of the SPAT device, with 32 images taken under distinct lighting conditions, requires a custom-made approach. Our proposed method consists of two parts: a neural network component that segments the wheals on the pixel level, followed by an algorithmic and interpretable approach for detecting and delineating the wheals. Results: We evaluate the performance of our method on a hold-out validation set of 217 patients. As a baseline we use a single conventionally lighted image per SPT as input to our method. Conclusion: Using the 32 SPAT images under various lighting conditions offers a considerably higher accuracy than a single image in conventional, uniform light.

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Improved Allergy Wheal Detection for the Skin Prick Automated Test Device

  • 会议论文

  • 发表日期：

    2025年

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  • Rembert Daems,
  • Sven Seys,
  • Valérie Hox,
  • Adam Chaker,
  • Glynnis De Greve,
  • Winde Lemmens,
  • Anne-Lise Poirrier,
  • Eline Beckers,
  • Zuzana Diamant,
  • Carmen Dierickx,
  • Peter W. Hellings,
  • Caroline Huart,
  • Claudia Jerin,
  • Mark Jorissen,
  • Hanne Oscé,
  • Karolien Roux,
  • Mark Thompson,
  • Sophie Tombu,
  • Saartje Uyttebroek,
  • Andrzej Zarowski,
  • Senne Gorris,
  • Laura Van Gerven,
  • Dirk Loeckx,
  • Thomas Demeester

摘要

Background: The skin prick test (SPT) is the gold standard for diagnosing sensitization to inhalant allergies. The Skin Prick Automated Test (SPAT) device was designed for increased consistency in test results, and captures 32 images to be jointly used for allergy wheal detection and delineation, which leads to a diagnosis. Materials and Methods: Using SPAT data from 868 patients with suspected inhalant allergies, we designed an automated method to detect and delineate wheals on these images. To this end, 10, 416 wheals were manually annotated by drawing detailed polygons along the edges. The unique data-modality of the SPAT device, with 32 images taken under distinct lighting conditions, requires a custom-made approach. Our proposed method consists of two parts: a neural network component that segments the wheals on the pixel level, followed by an algorithmic and interpretable approach for detecting and delineating the wheals. Results: We evaluate the performance of our method on a hold-out validation set of 217 patients. As a baseline we use a single conventionally lighted image per SPT as input to our method. Conclusion: Using the 32 SPAT images under various lighting conditions offers a considerably higher accuracy than a single image in conventional, uniform light.