| Prognostic significance of artificial intelligencequantified late gadolinium enhancement on cardiac magnetic resonance in hypertrophic cardiomyopathy |
Young-Sang Jeong1, Jong-Il Park1, Kang-Un Choi1, Jong-Ho Nam1, Chan-Hee Lee1
, Jang-Won Son1, Ung Kim1, Jong-Seon Park1, and Eunjung Kong2 |
1Division of Cardiology, Yeungnam University Medical Center, Daegu, Korea
2Department of Nuclear Medicine, Yeungnam University Medical Center, Daegu, Korea |
Corresponding Author:
Chan-Hee Lee , Tel: +82-53-620-3313, Fax: +82-53-620-3889, Email: chanheebox@naver.com
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Received: February 9, 2026; Revised: April 20, 2026; Accepted: May 11, 2026. |
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| 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).
Conclusions: 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. |
| Keywords:
Hypertrophic cardiomyopathy ; Magnetic resonance imaging ; Gadolinium ; Prognosis ; Artificial intelligence |
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