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TRUVACE RECORD VERSION
record: TRV-2026-1048
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-10T06:05:50.497498Z
status: published
lens: trace
sector: health
headline: Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease
dek: 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…
gain_title: 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_title: Visual HRCT scoring remains reader-dependent and AI outputs lack prospective multicenter validation and protocol harmonization needed to serve as treatment-triggering biomarkers.
trace_subject: AI-based HRCT quantification for systemic sclerosis-associated interstitial lung disease risk stratification and clinical decision support
gain_reading: 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.
gain_evidence: deep-learning usual interstitial pneumonia (UIP) probability stratifies FVC decline and long-term survival | artificial intelligence-derived HRCT parameters correlate with DLCO/TLC, predict mortality, and support disease-pattern classification
problem_reading: Visual HRCT scoring remains reader-dependent and AI outputs lack prospective multicenter validation and protocol harmonization needed to serve as treatment-triggering biomarkers.
problem_evidence: Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making | Prospective multicenter validation, protocol harmonization, calibration, version control, and integration into multidisciplinary discussion remain essential before artificial intelligence outputs can be used as treatment-triggering biomarkers
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.
limitation: Prospective multicenter validation, protocol harmonization, calibration, and integration into multidisciplinary discussion are still required before AI outputs can be used as treatment-triggering biomarkers.
tag: Dual reading
key_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.
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.
sources:
- peer_reviewed | Current Opinion in Rheumatology | https://doi.org/10.1097/bor.0000000000001201 | 2026-09-09
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