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Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning.

Ludovic Amruthalingam1,2, Oliver Buerzle3, Philippe Gottfrois1

  • 1Department of Biomedical Engineering, University of Basel, Basel, Switzerland.

Healthcare Informatics Research
|August 19, 2022
PubMed
Summary

A new deep learning model (DLM) accurately quantifies pustular psoriasis (PP) lesions from patient photos. This automated tool provides reliable and objective measurements of disease severity, aiding in treatment decisions.

Keywords:
Computer-Assisted DiagnosisDeep LearningDermatologyMachine LearningPsoriasis

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pustular psoriasis (PP) is a severe, chronic skin condition with challenging treatment.
  • Current disease severity assessment relies heavily on subjective clinical experience.
  • Pustules and brown spots are key indicators of PP activity.

Purpose of the Study:

  • To develop and validate an automated deep learning model (DLM) for quantifying PP lesions.
  • To objectively measure lesion count and surface area from patient photographs.
  • To improve the precision and reliability of PP disease activity evaluation.

Main Methods:

  • A deep learning model (DLM) was trained and validated on 121 patient photographs.
  • The DLM was tested on 30 unseen PP photographs and 213 out-of-distribution images of various pustular disorders.
  • Model performance was assessed using intraclass correlation coefficient (ICC) and Spearman correlation (SC).

Main Results:

  • The DLM achieved high agreement with expert labels on the test set (ICC: 0.97 for count, 0.93 for surface area).
  • On the pustular set, the DLM showed strong correlation with dermatologist severity rankings (SC: 0.66 for count, 0.80 for surface area).

Conclusions:

  • The proposed DLM reliably and automatically quantifies pustular psoriasis efflorescences.
  • This automated method enables precise and objective evaluation of PP disease activity.
  • The DLM has the potential to standardize PP severity assessment.