Machine Learning in Nonischemic Cardiomyopathy: Phenotyping, Mechanism Discovery, and Clinical Applications.

Area of Science:

• Cardiology
• Biomedical Informatics
• Computational Biology

Background:

• Nonischemic cardiomyopathy (NICM) presents diagnostic and risk stratification challenges due to its heterogeneous nature.
• Computational approaches integrating high-dimensional data offer novel insights into NICM patterns.
• Machine learning (ML) and deep learning (DL) are increasingly applied to complex cardiovascular diseases.

Purpose of the Study:

• To review recent studies (2020-2026) applying ML and DL to nonischemic cardiomyopathy.
• To synthesize findings across phenotype classification, mechanism discovery, and clinical decision support for NICM.
• To highlight the potential of computational methods for advancing NICM care.

Main Methods:

• Systematic review of literature on ML/DL applications in NICM (2020-2026).
• Analysis of studies focusing on imaging, electrocardiography, genomics, multiomics, and multimodal data.
• Categorization of applications into disease classification, mechanism discovery, and clinical decision support.

Main Results:

• ML/DL models using imaging and ECG enhance NICM subtype discrimination, arrhythmia detection, and early identification.
• Genomic and multiomics approaches improve variant interpretation and biomarker discovery for NICM.
• Multimodal frameworks show promise for NICM outcome prediction and personalized management.

Conclusions:

• ML and DL show significant potential in improving NICM diagnosis, understanding disease mechanisms, and guiding treatment.
• Challenges in generalizability, interpretability, and validation need addressing for clinical translation.
• Multimodal and longitudinal data integration is crucial for precision care in nonischemic cardiomyopathy.