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Jain H, Marsool MDM, Odat RM, Noori H, Jain J, Shakhatreh Z, Patel N, Goyal A, Gole S, Passey S. Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support. Cardiol Rev 2024:00045415-990000000-00260. [PMID: 38836621 DOI: 10.1097/crd.0000000000000708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.
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Affiliation(s)
- Hritvik Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | | | - Ramez M Odat
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Hamid Noori
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jyoti Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Zaid Shakhatreh
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nandan Patel
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Aman Goyal
- Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai, India
| | - Shrey Gole
- Department of Immunology and Rheumatology, Stanford University, CA; and
| | - Siddhant Passey
- Department of Internal Medicine, University of Connecticut Health Center, CT
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Toy J, Bosson N, Schlesinger S, Gausche-Hill M, Stratton S. Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review. Resusc Plus 2023; 16:100491. [PMID: 37965243 PMCID: PMC10641545 DOI: 10.1016/j.resplu.2023.100491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/23/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Background Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care. Methods We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care. Results Of 173 unique articles identified, 54 (31%) were included after screening. Of these studies, 15 (28%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38%) with additional studies from the United States (10/54, 19%), Korea (5/54, 10%), and Spain (3/54, 6%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44%), early dispatch-level detection and outcome prediction (7/54, 13%), return of spontaneous circulation and survival outcome prediction (15/54, 20%), and other (9/54, 16%). All but one study had a retrospective design. Conclusions A small but growing body of literature exists describing the use of AI to augment early OHCA care.
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Affiliation(s)
- Jake Toy
- University of California Los Angeles, Fielding School of Public Health, 650 Charles E Young Drive South, Los Angeles, CA 90095, USA
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Nichole Bosson
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Shira Schlesinger
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Marianne Gausche-Hill
- Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA
- Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA
- David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA
| | - Samuel Stratton
- University of California Los Angeles, Fielding School of Public Health, 650 Charles E Young Drive South, Los Angeles, CA 90095, USA
- Orange County California Emergency Medical Services Agency, 405 W. 5th Street, Santa Ana, CA 92705, USA
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Darginavicius L, Vencloviene J, Dobozinskas P, Vaitkaitiene E, Vaitkaitis D, Pranskunas A, Krikscionaitiene A. AI-Enabled Public Surveillance Cameras for Rapid Emergency Medical Service Activation in Out-of-Hospital Cardiac Arrests. Curr Probl Cardiol 2023; 48:101915. [PMID: 37392980 DOI: 10.1016/j.cpcardiol.2023.101915] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023]
Abstract
This study aims to evaluate the potential usefulness of a novel artificial intelligence (AI)-based video processing algorithm for rapidly activating ambulance services (EMS) in unwitnessed out-of-hospital cardiac arrest (OHCA) cases in public places. We hypothesized that AI should activate EMS using public surveillance cameras after detecting a person fallen due to OHCA. We created an AI model based on our experiment performed at the Lithuanian University of Health Sciences, Kaunas, Lithuania, in Spring 2023. Our research highlights the potential benefits of AI-based surveillance cameras for rapidly detecting and activating EMS during cardiac arrests.
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Affiliation(s)
- Linas Darginavicius
- Department of Disaster Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania.
| | - Jone Vencloviene
- Department of Environmental Sciences, Faculty of Natural Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Paulius Dobozinskas
- Department of Disaster Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Egle Vaitkaitiene
- Department of Disaster Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania; Department of Public Health, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Dinas Vaitkaitis
- Department of Disaster Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Andrius Pranskunas
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Asta Krikscionaitiene
- Department of Disaster Medicine, Lithuanian University of Health Sciences, Kaunas, Lithuania
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Mun E, Cho J. Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ. Tuberc Respir Dis (Seoul) 2023; 86:23-32. [PMID: 36288738 PMCID: PMC9816487 DOI: 10.4046/trd.2022.0087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/26/2022] [Indexed: 12/23/2022] Open
Abstract
Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.
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Affiliation(s)
- EunMi Mun
- Department of Software Engineering, Jeonbuk National University, Jeonju, Republic of Korea
| | - Jaehyuk Cho
- Department of Software Engineering, Jeonbuk National University, Jeonju, Republic of Korea,Address for correspondence Jaehyuk Cho, Ph.D. Department of Software Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea Phone 82-63-270-4771 Fax 82-63-270-4767 E-mail
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