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

Nonischemic cardiomyopathy (NICM) refers to a heterogeneous group of myocardial disorders whose shared phenotypes complicate diagnosis and risk stratification. This complexity has driven growing interest in computational approaches that can integrate high-dimensional data and capture patterns not evident through conventional analyses. In this review, we synthesize studies, published between 2020 and 2026, that apply machine learning and deep learning models to NICMs across 3 interconnected domains: phenotype classification, mechanism discovery, and clinical decision support. We find that imaging and electrocardiography-based models help improve subtype discrimination, arrhythmia detection, and early disease identification; genomic and multiomics approaches advance variant interpretation and biomarker discovery; and emerging multimodal frameworks extend these efforts toward outcome prediction and individualized management. Challenges remain in generalizability, interpretability, and prospective validation, as well as in integrating modalities into patient-level risk models. Continued development of multimodal and longitudinal approaches will be essential for translating these advances into precision care for NICM.