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Health·The Trace·Dual reading·Published 2026-09-10

AI-based HRCT quantification for systemic sclerosis-associated interstitial lung disease risk stratification and clinical decision support

Source article: Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

Abstract: Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of disease-related mortality in systemic sclerosis and the connective tissue disease-associated ILD (CTD-ILD) in which artificial intelligence imaging has advanced most rapidly. Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making. This review summarizes clinically relevant artificial intelligence and radiomics publications from approximately the last 18 months, foc…

TRV-2026-1048Peer-reviewedPermanent record — cite & verify
Trace impact reading

Negative state: both sides are scored from claims and sources, not community votes.

P 76The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 67The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

HR tomography of the chest of an IPF patient 2 by IPFeditor. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0

The quick read

This peer-reviewed review summarizes recent AI and radiomics work in systemic sclerosis-associated ILD, the leading cause of disease-related mortality in systemic sclerosis, where visual HRCT scoring is reader-dependent. It reports that deep-learning UIP probability and whole-chest quantitative biomarkers stratify FVC decline and survival, and that AI-derived HRCT parameters correlate with DLCO/TLC and support pattern classification across CTD-ILD subtypes.

The findings suggest AI quantification could become a credible adjunct for rheumatology and radiology practice, but deployment as a treatment-triggering biomarker remains premature. The review emphasizes that prospective multicenter validation, protocol harmonization, calibration, version control, and multidisciplinary integration are still needed to establish reliability and clinical utility.

Main points
  • In SSc-ILD, deep-learning UIP probability was reported to stratify FVC decline and long-term survival.
  • Whole-chest quantitative imaging biomarkers were described as extending risk prediction beyond lung involvement alone.
  • Automated SSc-specific segmentation, explainable Goh-equivalent scoring, slice-reduced radiomics, and open datasets were cited as strengthening the methodological base.
Gain

In systemic sclerosis-associated ILD, AI-based HRCT quantification stratifies FVC decline and long-term survival and correlates with lung function measures to predict mortality.

Problem

Visual HRCT scoring remains reader-dependent and AI outputs lack prospective multicenter validation and protocol harmonization needed to serve as treatment-triggering biomarkers.

The rundown

The review covers approximately the last 18 months of AI and radiomics literature, noting a shift from visual-score emulation toward outcome-oriented quantitative imaging biomarkers.

Parallel findings in RA-ILD and IIM-ILD and qCT definitions of progressive pulmonary fibrosis in broader fibrosing ILD were highlighted as conceptual advances relevant to future SSc-ILD trials.

The authors conclude the most defensible deployment model is human-in-the-loop decision support rather than autonomous treatment triggering.

What this doesn’t fix

Prospective multicenter validation, protocol harmonization, calibration, and integration into multidisciplinary discussion are still required before AI outputs can be used as treatment-triggering biomarkers.

Sources

Reader signal

How should this claim be treated?

The debate