Improved Allergy Wheal Detection for the Skin Prick Automated Test Device

TL;DR

This paper presents an automated method for detecting and delineating allergy wheals in skin prick test images, leveraging multiple lighting conditions to improve accuracy over traditional single-image approaches.

Contribution

The study introduces a novel two-part approach combining neural network segmentation with an interpretable algorithm, specifically designed for the multi-image data from the SPAT device.

Findings

Using 32 images under different lighting improves detection accuracy.

The method outperforms single-image baselines.

High annotation consistency achieved with detailed polygon labeling.

Abstract

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.