Clinical applications of artificial intelligence and machine learning in neurocardiology - a comprehensive review

Aug 5, 2026

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Neurocardiology focuses on the bidirectional relationship between the nervous and cardiovascular systems, explaining how cardiac pathology can lead to cerebrovascular disease through the Heart–Brain axis and how neurologic disorders can produce cardiac injury via the Brain–Heart axis. Timely assessment and management of stroke and related cardiac conditions are central to improving patient outcomes. This review synthesizes widely used and emerging AI/ML algorithms for common cardiac sources of stroke, cryptogenic strokes, and stroke-related cardiac disease, highlighting accurate tools that support prediction, identification, prognosis, and management, while noting remaining needs for larger datasets and training.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

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Introduction

Neurocardiology

The heart-brain axis: cerebrovascular disease secondary to cardiac pathology

TYPE Review PUBLISHED 03 April 2025 DOI 10.3389/fcvm.2025.1525966 EDITED BY Leonardo Roever, Brazilian Evidence-Based Health Network, Brazil REVIEWED BY Andre Rodrigues Duraes, Federal University of Bahia (UFBA), Brazil Virender Ranga, Delhi Technological University, India *CORRESPONDENCE Reza Dashti [reza.dashti@stonybrookmedicine.edu](reza.dashti@stonybrookmedicine.edu) RECEIVED 11 November 2024 ACCEPTED 20 March 2025 PUBLISHED 03 April 2025 CITATION Basem J, Mani R, Sun S, Gilotra K, DianatiMaleki N and Dashti R (2025) Clinical applications of artiﬁcial intelligence and machine learning in neurocardiology: a comprehensive review. Front. Cardiovasc. Med. 12:1525966 . doi: 10.3389/fcvm.2025.1525966 COPYRIGHT © 2025 Basem, Mani, Sun, Gilotra, DianatiMaleki and Dashti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Clinical applications of artiﬁcial intelligence and machine learning in neurocardiology: a comprehensive review Jade Basem1, Racheed Mani2, Scott Sun1, Kevin Gilotra1, Neda Dianati-Maleki3 and Reza Dashti4* 1Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, 2Department of Neurology, Stony Brook University Hospital, Stony Brook, NY, United States, 3Department of Medicine, Division of Cardiovascular Medicine, Stony Brook University Hospital, Stony Brook, NY, United States, 4Department of Neurosurgery, Stony Brook University Hospital, Stony Brook, NY, United States Neurocardiology is an evolving ﬁeld focusing on the interplay between the nervous system and cardiovascular system that can be used to describe and understand many pathologies. Acute ischemic stroke can be understood through this framework of an interconnected, reciprocal relationship such that ischemic stroke occurs secondary to cardiac pathology (the Heart-Brain axis), and cardiac injury secondary to various neurological disease processes (the Brain-Heart axis) . The timely assessment, diagnosis, and subsequent management of cerebrovascular and cardiac diseases is an essential part of bettering patient outcomes and the progression of medicine. Artiﬁcial intelligence (AI) and machine learning (ML) are robust areas of research that can aid diagnostic accuracy and clinical decision making to better understand and manage the disease of neurocardiology. In this review, we identify some of the widely utilized and upcoming AI/ML algorithms for some of the most common cardiac sources of stroke, strokes of undetermined etiology, and cardiac disease secondary to stroke. We found numerous highly accurate andefﬁcient AI/ML products that, when integrated, provided improved efﬁcacy for disease prediction, identiﬁcation, prognosis, and management within the sphere of stroke and neurocardiology. In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. Our review shows an evident interest and exciting new frontier for neurocardiology with artiﬁcial intelligence and machine learning. KEYWORDS artiﬁcial intelligence, machine learning, deep learning, cerebrovascular, ischemic stroke, cardiovascular, neurocardiology Frontiers in Cardiovascular Medicine 01 [frontiersin.org](frontiersin.org) 1 Introduction 1.1

FAQs

What is the main clinical focus of neurocardiology in relation to stroke?

Neurocardiology emphasizes the interplay between the nervous and cardiovascular systems, including the Heart–Brain axis (cardiac pathology leading to stroke) and the Brain–Heart axis (neurologic disease affecting the heart).

How do AI and machine learning contribute to neurocardiology care in this review?

AI/ML can improve diagnostic accuracy and clinical decision-making by supporting disease prediction, identification, prognosis, and management for stroke and neurocardiology conditions.

Which stroke etiologies are highlighted for AI/ML algorithm development?

The review covers cardiac sources of stroke, strokes of undetermined etiology (including ESUS and cryptogenic strokes), and cardiac disease occurring secondary to stroke, along with promising research on underlying cardiac causes and secondary prevention.

Aug 5, 2026

Upload by Rowan