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Related Experiment Video

Updated: Dec 26, 2025

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High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

Chase Correia1, Seamus Mawe2, Shane Lofgren3

  • 1Department of Internal Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Arthritis Research & Therapy
|March 16, 2020
PubMed
Summary

Deep neural network (DNN) analysis of skin biopsies offers a novel, objective method for assessing systemic sclerosis (SSc) fibrosis. This computer vision approach accurately correlates with existing clinical scores, improving SSc outcome measurement.

Keywords:
AlexNetComputer visionDeep neural networkHistologyModified Rodnan skin scoreOutcome measuresOutcomesQuantitative image featuresSclerodermaSystemic sclerosis

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Area of Science:

  • Dermatology and Computational Pathology
  • Biomarkers and Outcome Measures in Systemic Sclerosis

Background:

  • Skin fibrosis, characterized by dermal collagen deposition, is a key feature of systemic sclerosis (SSc).
  • The modified Rodnan skin score (mRSS) is a standard clinical trial outcome measure for SSc skin involvement but suffers from subjectivity and confounding factors.
  • There is a need for more objective and reproducible histopathological outcome measures in SSc research.

Purpose of the Study:

  • To develop and validate a novel histopathological outcome measure for SSc using deep neural network (DNN) analysis of skin biopsies.
  • To assess the ability of DNN analysis to reliably evaluate skin stiffness (mRSS) and differentiate SSc from normal skin.
  • To establish the correlation of DNN-derived scores with established clinical and histopathological measures of SSc severity.

Main Methods:

  • Analysis of trichrome-stained skin biopsy sections from independent SSc cohorts using the AlexNet DNN.
  • Extraction of 4096 quantitative image features (QIFs) per biopsy, summarized using principal component analysis (PCA) or directly used in regression models.
  • Development of DNN-based Biopsy Scores, Diagnostic Scores, and Fibrosis Scores to assess SSc status and fibrosis severity.

Main Results:

  • DNN-derived Biopsy Scores significantly correlated with the modified Rodnan skin score (mRSS) in the primary cohort (R=0.55, p=0.01).
  • In the secondary cohort, DNN analysis achieved high accuracy in discriminating SSc from control biopsies (misclassification rate < 7%) and accurately estimated mRSS (R=0.70 training, R=0.55 test).
  • The DNN-derived Fibrosis Score demonstrated strong correlation with the Scleroderma Skin Severity Score (4S) (R=0.69) and longitudinal mRSS changes.

Conclusions:

  • Deep neural network (DNN) analysis provides an unbiased, quantitative, and reproducible method for evaluating skin fibrosis in systemic sclerosis.
  • This computational approach shows significant association with validated SSc outcome measures, offering a promising new tool for clinical trials and research.
  • DNN analysis of skin biopsies represents a significant advancement in objective histopathological assessment for SSc.