This review focuses on cardiovascular digital twins — patient-specific computational models supporting diagnosis, prognosis, and treatment optimization. It covers mechanistic, data-driven, and hybrid methods integrating physics constraints with graph-based learning. Challenges in data assimilation, validation, and clinical translation are discussed.
The review is relevant for modeling complex biological systems, illustrating general principles for building digital twins from heterogeneous data.
Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.
