Pathological classification of non-ischaemic dilated cardiomyopathy based on deep learning.

ABSTRACT:

Aims

Non-ischaemic dilated cardiomyopathy (NIDCM) is a major cause of heart failure (HF) and heart transplantation (HTx), characterized by heterogeneity in aetiology, clinical phenotype, and disease progression. Nevertheless, precision medicine-based diagnostics and treatment strategies for NIDCM remain lacking. This proof-of-concept study aimed to stratify NIDCM patients by pathological features and identify those at high-risk for malignant arrhythmia (MA) and rapid progression to end-stage HF.

Methods and results

293 NIDCM-HTx patients were included in this study. A total of 3516 heart tissue slides from six representative sites of each patient were analyzed using deep learning-based computational pathology (DL-CPath) and unsupervised clustering to identify pathological su

SUBMITTER: Jia H 

PROVIDER: S-EPMC12821066 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature