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Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

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Image-based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks.

M J Schaap¹, N J Cardozo^1,2, A Patel²

¹Department of Dermatology, Radboud University Medical Center, Nijmegen, The Netherlands.

Journal of the European Academy of Dermatology and Venereology : JEADV

|October 15, 2021

Summary

This summary is machine-generated.

Automated Psoriasis Area and Severity Index (PASI) scoring using Convolutional Neural Networks (CNNs) shows potential for objective disease assessment. CNNs performed comparably to physicians in scoring erythema, desquamation, and induration, and outperformed them in area scoring.

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Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

The Psoriasis Area and Severity Index (PASI) is crucial for monitoring psoriasis severity and treatment effectiveness.
Automating PASI scoring with deep learning, specifically Convolutional Neural Networks (CNNs), offers potential for objective and efficient assessments.

Purpose of the Study:

To evaluate the performance of CNNs in automated, image-based PASI scoring across different anatomical regions.
To compare the accuracy of CNN-based PASI scoring with that of human physicians.

Main Methods:

CNNs were trained on standardized imaging datasets of trunk, arm, and leg regions for individual PASI subscores (erythema, desquamation, induration, area).
Physicians also retrospectively scored trunk images to enable direct comparison.
Agreement was quantified using intraclass correlation coefficients (ICCs).

Main Results:

CNNs demonstrated good agreement with real-life PASI scores, with ICCs for the trunk region ranging from 0.580 to 0.793.
CNN performance was comparable or slightly superior to physicians in image-based scoring for erythema, induration, and area.
Physicians showed slightly better performance than CNNs in desquamation scoring.

Conclusions:

CNNs show significant potential for objective, automated, image-based PASI scoring at the anatomical region level.
The findings suggest CNNs can serve as a valuable tool in clinical practice and research for psoriasis assessment.
CNNs achieved comparable or superior performance to physicians in specific PASI subscore evaluations.