ArticleThe Korean journal of internal medicine2026
Prognostic significance of artificial intelligence-quantified late gadolinium enhancement on cardiac magnetic resonance in hypertrophic cardiomyopathy.
Article in The Korean journal of internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsThis study aimed to evaluate the prognostic value of artificial intelligence (AI)-based late gadolinium enhancement (LGE) quantification using cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM).
methodsWe retrospectively analyzed 142 patients with HCM (mean age 58.5 ± 13.7 yr; 72.5% men) who underwent CMR at Yeungnam University Medical Center between 2015 and 2023. LGE was quantified using an AI-based segmentation algorithm with a 6-standard deviation method. Patients were stratified into a high LGE group (≥ 15%) and a low LGE group (< 15%). The primary outcome was a composite of cardiovascular death, including sudden cardiac death (SCD) and SCD-equivalent events.
resultsOver a median follow-up of 59 months, the high LGE group experienced a higher incidence of the primary outcome compared with the low LGE group (21.2% vs. 4.6%, p = 0.0067). After adjustment for age and left ventricular ejection fraction, a high AI-quantified LGE burden (≥ 15%) remained independently associated with the primary outcome (adjusted hazard ratio 4.67, 95% confidence interval 1.43-15.30, p = 0.011). Receiver operating characteristic analysis determined an optimal LGE cutoff of 8% for predicting the primary outcome (area under the curve 0.821). Patients with LGE ≥ 8% exhibited markedly increased rates of the primary outcome (18.2% vs. 0%, p < 0.001) and SCD/SCD-equivalent events (13.6% vs. 0%, p = 0.00074).
conclusionAI-based quantitative assessment of LGE is an independent predictor of adverse cardiovascular outcomes in patients with HCM and may represent a clinically meaningful imaging biomarker for risk stratification.
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.