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Review
. 2023 May 10;2(3):100323.
doi: 10.1016/j.jacadv.2023.100323. eCollection 2023 May.

Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement

Giselle M Statz  1 , Aron Z Evans  2 , Samuel L Johnston  1 , Mehul Adhaduk  2 , Avinash R Mudireddy  3 , Milan Sonka  3 , Sangil Lee  4 , E John Barsotti  5 , Fabrizio Ricci  6 , Franca Dipaola  7 , Madeleine Johansson  8 , Robert S Sheldon  9 , Venkatesh Thiruganasambandamoorthy  10 , Rose-Anne Kenny  11 , Tyler C Bullis  2 , Deepak K Pasupula  12 , Jon Van Heukelom  4 , Milena A Gebska  2 , Brian Olshansky  2
Affiliations
Review

Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement

Giselle M Statz et al. JACC Adv. .

Abstract

Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.

Keywords: artificial intelligence; emergency department; machine learning; syncope; transient loss of consciousness.

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Conflict of interest statement

This research was funded by the Iowa Initiative for Artificial Intelligence (IIAI), Carver College of Medicine Office of Research, University of Iowa. Dr Olshansky is on the Data and Safety Monitoring Board of AstraZeneca. Dr Sonka is an inventor and has patents and patent applications in computer vision and medical image analysis; and is a cofounder of Medical Imaging Applications, LLC, Coralville, Iowa, USA and VIDA Diagnostics, Inc, Coralville, Iowa, USA. Dr Venkatesh Thiruganasambandamoorthy is supported through a Physicians’ Services Incorporated Foundation Mid-Career Clinical Researcher award and University of Ottawa Tier-1 Clinical Research Chair in Cardiovascular Emergencies award. Dr Thiruganasambandamoorthy has received peer-reviewed grant funds for studies from the following governmental or non-profit agencies: the 10.13039/501100000241Physicians’ Services Incorporated Foundation—Ontario, Canada, 10.13039/501100000024Canadian Institutes of Health Research, 10.13039/100004411Heart and Stroke Foundation Canada, and the Cardiac Arrhythmia Network of Canada as part of the Networks of Centres of Excellence (NCE; and is a consultant for the NIH funded Practical Approaches to Care in Emergency Syncope (PACES) study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

None
Graphical abstract
Figure 1
Figure 1
Syncope in the Context of Transient Loss of Consciousness Adapted from the 2018 European Society of Cardiology Guidelines for the Diagnosis and Management of Syncope. TIA = transient ischemic attack; TLOC = transient loss of consciousness.
Figure 2
Figure 2
Short- and Long-Term Risk Factors in Syncope Patients Adapted from the 2017 ACC/AHA/HRS Guideline for the Evaluation and Management of Patients with Syncope.
Figure 3
Figure 3
Steps Involved in Supervised Machine Learning (Top row) A supervised classifier is trained on different types of vehicles (eg, car, truck, and motorcycle). (Bottom row) When given a new set of vehicles of different colors/model types, the trained classifier can assign the correct vehicle label.
Central Illustration
Central Illustration
Harnessing AI to Improve Syncope Management This diagram outlines clinical objectives and proposed steps of AI-based initiatives to improve syncope management. Created with Biorender.com. AI = artificial intelligence; OH = orthostatic hypotension; TLOC = transient loss of consciousness; VVS = vasovagal syncope.
Figure 4
Figure 4
Developing a ML Model for Syncope Proposed steps to build a ML model for syncope. (First row) Emphasizing importance of large scale, prospective, and international datasets. (Second row) Potential inputs (left) and outputs (right) of a ML model to address the main clinical objectives in syncope management while recognizing underlying ethical principles. (Third row) ML pipeline showing sequential stages of a supervised ML project. (Fourth row) Descriptions of each stage in the ML pipeline. Created with Flaticon.com and Biorender.com. TLOC = transient loss of consciousness.
See this image and copyright information in PMC

References

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