Healthc Inform Res.  2022 Jul;28(3):222-230. 10.4258/hir.2022.28.3.222.

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

Affiliations
  • 1Department of Biomedical Engineering, University of Basel, Basel, Switzerland
  • 2Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
  • 3Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zurich, Switzerland
  • 4Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland
  • 5Department of Dermatology, University Hospital of Basel, Basel, Switzerland

Abstract


Objectives
Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs.
Methods
In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was trained and validated with 121 photographs, keeping 30 photographs as a test set to assess the DLM performance on unseen data. We also evaluated our DLM on 213 unstandardized, out-of-distribution photographs of various pustular disorders (referred to as the pustular set), which were ranked from 0 (no disease) to 4 (very severe) by one dermatologist for disease severity. The agreement between the DLM predictions and experts’ labels was evaluated with the intraclass correlation coefficient (ICC) for the test set and Spearman correlation (SC) coefficient for the pustular set.
Results
On the test set, the DLM achieved an ICC of 0.97 (95% confidence interval [CI], 0.97–0.98) for count and 0.93 (95% CI, 0.92–0.94) for surface percentage. On the pustular set, the DLM reached a SC coefficient of 0.66 (95% CI, 0.60–0.74) for count and 0.80 (95% CI, 0.75–0.83) for surface percentage.
Conclusions
The proposed method quantifies efflorescences from PP photographs reliably and automatically, enabling a precise and objective evaluation of disease activity.

Keyword

Psoriasis; Dermatology; Computer-Assisted Diagnosis; Machine Learning; Deep Learning

Figure

  • Figure 1 Sample image (A) with expert labels (B) and the DLM prediction (C). This picture came from the test set used to evaluate the DLM and was not used in the training process. The original image is shown in (A), while (B) shows the image overlaid with expert labels and (C) the image overlaid with the DLM predictions. The pustules are colored in yellow, the brown spots in red, the patient’s skin in blue, and the background in violet. DLM: deep learning model.

  • Figure 2 Agreement of DLM lesion count predictions with expert labels. The figure shows the Bland-Altman plots of the predicted count for pustules (A), spots (C), and combined lesions (E). The plots for pustules (B), spots (D), and both lesions (F) show the third quartile of the mean difference and the mean absolute difference of the predicted count for patches with up to the number of lesions specified on the horizontal axis value. DLM: deep learning model.

  • Figure 3 Agreement of DLM lesion surface predictions with expert labels. The figure shows the Bland-Altman plots of the predicted surface percentage for pustules (A), spots (C) and combined lesions (E). The plots for pustules (B), spots (D), and both lesions (F) show the third quartile of the mean difference and the mean absolute difference of the predicted surface percentage for patches with up to the lesion surface specified on the horizontal axis value. DLM: deep learning model.


Reference

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