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Related Concept Videos

EPS and iPS Cells in Disease Research01:21

EPS and iPS Cells in Disease Research

Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...

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Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.

Kai Huang1,2,3,4, Xian Wu5, Yixin Li1,2,3,4

  • 1Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China.

Journal of Medical Internet Research
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

An AI system accurately estimates Psoriasis Area and Severity Index (PASI) scores using images, outperforming dermatologists. This tool aids in objective psoriasis severity assessment and patient self-management.

Keywords:
PASIPsoriasis Area and Severity Indexartificial intelligencechronic diseasedeep learning systemdermatologydesigninflammationmanagementmobile appmodelpsoriasispsoriasis severity assessmenttoolsusers

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Psoriasis is a common inflammatory skin condition.
  • Objective assessment of psoriasis severity is challenging due to the subjectivity of current metrics like the Psoriasis Area and Severity Index (PASI).
  • Tele-dermatology requires reliable tools for objective severity assessment.

Purpose of the Study:

  • To develop and validate an image-based artificial intelligence (AI) system for objective psoriasis severity assessment.
  • To facilitate the long-term management of psoriasis patients through accurate and efficient severity scoring.
  • To overcome the limitations of subjective PASI scoring in clinical practice.

Main Methods:

  • A deep learning system was trained on 14,096 psoriasis images from 2,367 patients to estimate PASI scores.
  • A multiview feature enhancement block and a combined classification-regression header with a cross-teacher header were utilized for improved accuracy.
  • The model's performance was evaluated using Mean Absolute Error (MAE) and compared against 43 experienced dermatologists.

Main Results:

  • The AI model achieved a 33.2% performance gain over the average of 43 dermatologists in estimating overall PASI scores.
  • The model demonstrated a minimum MAE of 2.05 with three input images, indicating high accuracy in severity prediction.
  • The SkinTeller app, incorporating the AI model, was utilized 3,369 times across 1,497 patients and received positive feedback from users.

Conclusions:

  • An image-AI-based system can automatically calculate PASI scores efficiently, objectively, and accurately.
  • The SkinTeller app shows promise as an alternative tool for dermatologists' assessments and for psoriasis patient self-management.
  • AI-driven tools can enhance the objective evaluation and management of chronic skin conditions like psoriasis.