ArticleBMC pulmonary medicine2026
AI-based chest CT quantification of interstitial lung disease in idiopathic inflammatory myopathies: agreement with expert visual assessment in 107 patients.
Article in BMC pulmonary medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterstitial lung disease (ILD) is a determinant of morbidity and mortality in idiopathic inflammatory myopathies (IIM), but chest HRCT evaluation remains observer-dependent. Artificial intelligence (AI) may provide reproducible quantitative assessment. We compared AI-based quantification of ILD with expert visual scoring in IIM.
methodsIn this monocentric retrospective study, 107 patients with IIM-associated ILD from a national myositis registry were included. One representative chest HRCT per patient was evaluated by a thoracic radiologist using a semi-quantitative lobar score and by a commercially available AI tool for lung texture analysis. AI-derived volumes were converted to the same 5-point scale as the visual score. Correlations were assessed with Spearman coefficients and agreement with Cohen’s kappa.
resultsAll CTs were suitable for visual assessment and 106/107 (99%) for AI analysis. AI identified ground-glass opacities (GGO) as the predominant abnormality, with a lower-lobe predominance. Correlations between AI and radiologist scores were strong for normal lung (r = 0.77) and moderate for GGO (r = 0.64) and consolidation (r = 0.60), but weaker for reticulations (r = 0.34) and honeycombing (r = 0.42). Agreement was good for GGO (κ = 0.70) and consolidation (κ = 0.60), moderate for reticulations (κ = 0.37) and low for honeycombing (κ = 0.16).
conclusionIn IIM-associated ILD, AI-based chest HRCT quantification showed good agreement with expert visual assessment, particularly for GGO and consolidation, but was less reliable for complex fibrotic patterns. AI may support more objective and reproducible evaluation of interstitial involvement, as a complement to expert interpretation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.