Korean J Intern Med. 2026 Sep;41(5):884-893. doi: 10.3904/kjim.2026.065. Epub 2026 Sep 1.
ABSTRACT
BACKGROUND/AIMS: This 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).
METHODS: We 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.
RESULTS: Over 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).
CONCLUSION: AI-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.
PMID:42754524 | DOI:10.3904/kjim.2026.065