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Observer-independent assessment of psoriasis-affected area using machine learning.

N Meienberger1, F Anzengruber1, L Amruthalingam2

  • 1Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.

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|October 9, 2019
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Summary

A new machine learning algorithm offers objective psoriasis assessment, achieving over 90% accuracy in 77% of images. This technology could provide a reliable alternative to subjective human evaluation for psoriasis severity.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Machine Learning

Background:

  • Psoriasis severity assessment is subjective and lacks objective tools.
  • High-cost psoriasis therapies require accurate, objective severity measures for reimbursement.

Purpose of the Study:

  • Develop an objective psoriasis assessment method using machine learning image segmentation.
  • Establish an accurate and reproducible tool for evaluating psoriasis severity.

Main Methods:

  • Retrospective diagnostic accuracy study using 259 standardized patient photographs.
  • Machine learning algorithm trained and validated on 203 images, tested on 56.
  • Algorithm's lesion area assessment compared to manual markings and dermatologist estimates.

Main Results:

  • Algorithm achieved >90% accuracy in 77% of tested images.
  • Average difference between algorithm and manual marking was 5.9%.
  • Average difference between algorithm and physician estimates was 8.1%.

Conclusions:

  • Machine learning technology shows significant potential for objective psoriasis assessment.
  • This method offers an objective alternative to the subjective Psoriasis Area and Severity Index (PASI).
  • Further development is recommended to establish this as a reliable assessment tool.