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TRV-2026-1048Certified recordPeer-reviewed

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

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…

Health · The Trace — both readings · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — 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.

Current reading — problem

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

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.

Evidence

Reader signal

How should this claim be treated?

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Truvace Impact Record TRV-2026-1048, v1: “Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease.” Truvace, 2026-09-10. /record/TRV-2026-1048 (accessed at citation time). sha256 f809d8618d3b50fb…

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