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A review of psoriasis image analysis based on machine learning.

Huihui Li¹, Guangjie Chen¹, Li Zhang^2,3

¹School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.

Frontiers in Medicine

|August 22, 2024

Summary

Machine learning (ML) aids psoriasis diagnosis by analyzing patterns in data. This review summarizes ML applications in lesion analysis over the last decade, identifying common models and future research directions.

Keywords:

deep learning dermatology machine learning psoriasis review

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Area of Science:

Artificial Intelligence (AI) and Machine Learning (ML) in medical diagnostics.
Computational dermatology and image analysis for skin disease assessment.

Background:

Psoriasis diagnosis and management are critical areas in dermatology.
Machine learning offers advanced computational approaches for medical image analysis.
The integration of AI techniques like Traditional Machine Learning (TML) and Deep Learning (DL) is transforming healthcare processes.

Purpose of the Study:

To systematically review the research and application of ML in psoriasis analysis over the past decade.
To summarize key findings, common models, datasets, challenges, and future trends in ML for psoriasis.
To provide insights for future research directions in this domain.

Main Methods:

Systematic literature review of 53 publications.
Searches conducted across major scientific databases: Web of Science, PubMed, and IEEE Xplore.
Publications classified into three main categories: lesion localization/segmentation, lesion recognition, and lesion severity/area scoring.

Main Results:

Identified and summarized 53 relevant publications on ML in psoriasis analysis from the last decade.
Categorized ML applications into lesion localization, recognition, and severity scoring.
Detailed common ML models and datasets used in psoriasis analysis.

Conclusions:

Machine learning shows significant promise in improving psoriasis diagnosis and analysis.
Key challenges and future trends in ML for psoriasis were identified.
The review provides a foundation for future research and development in AI-driven dermatological applications.