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Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions.

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Artificial intelligence (AI) offers new ways to assess systemic sclerosis (SSc) by improving imaging analysis and disease phenotyping. While promising, AI tools need more validation for clinical use in this rare autoimmune disease.

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Area of Science:

  • Rheumatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Systemic sclerosis (SSc) is a rare autoimmune disease causing fibrosis and organ damage.
  • Interstitial lung disease (ILD) and cardiac issues are major causes of mortality in SSc.
  • Current SSc assessment methods lack objectivity and reproducibility.

Purpose of the Study:

  • To review current artificial intelligence (AI) applications in systemic sclerosis (SSc).
  • To evaluate the potential of AI in disease phenotyping, risk stratification, and imaging quantification.
  • To identify limitations and future directions for AI implementation in SSc.

Main Methods:

  • Review of AI techniques including machine learning, deep learning, and radiomics in SSc.
  • Analysis of AI applications in high-resolution CT, nailfold capillaroscopy, and skin imaging.
  • Critical examination of studies focusing on AI for fibrosis segmentation, microvascular staging, and dermal remodelling.

Main Results:

  • AI shows potential in automated fibrosis detection (SSc-ILD), microvascular staging (capillaroscopy), and skin assessment.
  • AI tools can enhance disease phenotyping and risk stratification in SSc.
  • Most AI applications are exploratory, with limited validation and data.

Conclusions:

  • AI offers promising advancements for systemic sclerosis assessment, particularly in imaging and phenotyping.
  • Significant challenges remain, including data limitations, heterogeneity, and the need for external validation.
  • Future research should focus on multicenter, ethically governed AI implementation for clinical translation.