TY - JOUR
T1 - Artificial intelligence and heart failure
T2 - A state-of-the-art review
AU - Khan, Muhammad Shahzeb
AU - Arshad, Muhammad Sameer
AU - Greene, Stephen J.
AU - Van Spall, Harriette G.C.
AU - Pandey, Ambarish
AU - Vemulapalli, Sreekanth
AU - Perakslis, Eric
AU - Butler, Javed
N1 - Publisher Copyright:
© 2023 European Society of Cardiology.
PY - 2023/9
Y1 - 2023/9
N2 - Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
AB - Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
KW - Artificial intelligence
KW - Diagnosis
KW - Heart failure
KW - Phenomapping
KW - Risk stratification
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U2 - 10.1002/ejhf.2994
DO - 10.1002/ejhf.2994
M3 - Review article
C2 - 37560778
AN - SCOPUS:85168870329
SN - 1388-9842
VL - 25
SP - 1507
EP - 1525
JO - European Journal of Heart Failure
JF - European Journal of Heart Failure
IS - 9
ER -