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Predicting psoriasis severity using machine learning: a systematic review.

Eric P McMullen¹, Yousif A Al Naser^2,3, Mahan Maazi⁴

¹Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.

Clinical and Experimental Dermatology

|August 22, 2024

Summary

This summary is machine-generated.

Machine learning (ML) shows promise in predicting psoriasis severity, primarily using image-based models. However, limited prospective clinical applications and data heterogeneity necessitate further large-scale trials for AI to advance psoriasis management.

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Area of Science:

Dermatology
Artificial Intelligence
Machine Learning

Background:

Machine learning (ML) applications in dermatology extend beyond skin lesion diagnosis.
A systematic review gap exists for ML in predicting psoriasis severity.

Purpose of the Study:

To systematically review literature on ML algorithms for psoriasis severity prediction.
To identify limitations in current clinical applications of these ML tools.

Main Methods:

Comprehensive search of Embase, MEDLINE, ACM Digital Library, Scopus, and IEEE Xplore.
Inclusion criteria applied to articles from inception to August 2024.

Main Results:

30 articles were included; 1 used serum biomarkers, 29 used image-based models.
Psoriasis Area and Severity Index (PASI) was the most common score (15 articles), followed by body surface area (5 articles).

Conclusions:

Small study size and heterogeneity limit current review findings.
ML/AI show potential for psoriasis management, particularly in non-image-based applications.
Large-scale prospective trials with diverse image data are crucial for validating AI predictive models.