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Soroudi S, Jaafari MR, Arabi L. Lipid nanoparticle (LNP) mediated mRNA delivery in cardiovascular diseases: Advances in genome editing and CAR T cell therapy. J Control Release 2024; 372:S0168-3659(24)00371-7. [PMID: 38876358 DOI: 10.1016/j.jconrel.2024.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/05/2024] [Accepted: 06/09/2024] [Indexed: 06/16/2024]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of global mortality among non-communicable diseases. Current cardiac regeneration treatments have limitations and may lead to adverse reactions. Hence, innovative technologies are needed to address these shortcomings. Messenger RNA (mRNA) emerges as a promising therapeutic agent due to its versatility in encoding therapeutic proteins and targeting "undruggable" conditions. It offers low toxicity, high transfection efficiency, and controlled protein production without genome insertion or mutagenesis risk. However, mRNA faces challenges such as immunogenicity, instability, and difficulty in cellular entry and endosomal escape, hindering its clinical application. To overcome these hurdles, lipid nanoparticles (LNPs), notably used in COVID-19 vaccines, have a great potential to deliver mRNA therapeutics for CVDs. This review highlights recent progress in mRNA-LNP therapies for CVDs, including Myocardial Infarction (MI), Heart Failure (HF), and hypercholesterolemia. In addition, LNP-mediated mRNA delivery for CAR T-cell therapy and CRISPR/Cas genome editing in CVDs and the related clinical trials are explored. To enhance the efficiency, safety, and clinical translation of mRNA-LNPs, advanced technologies like artificial intelligence (AGILE platform) in RNA structure design, and optimization of LNP formulation could be integrated. We conclude that the strategies to facilitate the extra-hepatic delivery and targeted organ tropism of mRNA-LNPs (SORT, ASSET, SMRT, and barcoded LNPs) hold great prospects to accelerate the development and translation of mRNA-LNPs in CVD treatment.
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Affiliation(s)
- Setareh Soroudi
- School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran; Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahmoud Reza Jaafari
- Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Pharmaceutical Nanotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran; Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Leila Arabi
- Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Pharmaceutical Nanotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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Ragosta M. What Is the Score?: Predicting Success or Failure in Chronic Total Occlusion Intervention. JACC Cardiovasc Interv 2024; 17:1385-1387. [PMID: 38703150 DOI: 10.1016/j.jcin.2024.03.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Michael Ragosta
- University of Virginia Health System, Charlottesville, Virginia, USA.
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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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Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
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Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med 2024; 151:102866. [PMID: 38593684 DOI: 10.1016/j.artmed.2024.102866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 03/20/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Abstract
An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.
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Affiliation(s)
- Sanjeevi G
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India
| | - Uma Gopalakrishnan
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India.
| | | | - Thushara Madathil
- Department of Cardiac Anesthesiology, Amrita Institute of Medical Sciences and Research Center, Kochi, India
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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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Sharma A, Medapalli T, Alexandrou M, Brilakis E, Prasad A. Exploring the Role of ChatGPT in Cardiology: A Systematic Review of the Current Literature. Cureus 2024; 16:e58936. [PMID: 38800264 PMCID: PMC11124467 DOI: 10.7759/cureus.58936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Chat Generative Pre-Trained Transformer (ChatGPT) is a chatbot based on a large language model that has gained public interest since its release in November 2022. This systematic review examines the current literature on the potential applications of ChatGPT in cardiology. A systematic literature search was conducted to retrieve all publications on ChatGPT in PubMed, Scopus, MedRxiv, and the Cochrane Library published on or before September 30, 2023. Search terms relating to ChatGPT and cardiology were used. Publications without relevance to ChatGPT and cardiology were excluded. The included publications were divided into cohorts. Cohort A examined ChatGPT's role in improving patient health literacy. Cohort B explored ChatGPT's role in clinical care. Cohort C examined ChatGPT's role in future literature and research. Cohort D included case reports that used ChatGPT. A total of 115 publications were found across all databases. Twenty-four publications met the inclusion criteria and were included in the review. Cohort A-C included a total of 14 records comprised of editorials/letters to the editor (29%), research letters/correspondence (21%), review papers (21%), observational studies (7%), research studies (7%), and short reports (7%). Cohort D included 10 case reports. No relevant systematic literature reviews, meta-analyses, or randomized controlled trials were identified in the search. Based on this review of the literature, ChatGPT has the potential to enhance patient education, support clinicians providing clinical care, and enhance the development of future literature. However, further studies are needed to understand the potential applications of ChatGPT in cardiology and to address ethical concerns regarding the delivery of medical advice and the authoring of manuscripts.
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Affiliation(s)
- Aditi Sharma
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
| | - Tejas Medapalli
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
| | | | | | - Anand Prasad
- Department of Medicine, Division of Cardiology, University of Texas (UT) Health San Antonio, San Antonio, USA
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Alshanberi AM, Mousa AH, Hashim SA, Almutairi RS, Alrehali S, Hamisu AM, Shaikhomer M, Ansari SA. Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024; 16:S1815-S1820. [PMID: 38882896 PMCID: PMC11174240 DOI: 10.4103/jpbs.jpbs_1162_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/21/2024] [Accepted: 02/01/2024] [Indexed: 06/18/2024] Open
Abstract
Background Mounting research suggests that artificial intelligence (AI) is one of the innovations that aid in the patient's diagnosis and treatment, but unfortunately limited research has been conducted in this regard in the Kingdom of Saudi Arabia (KSA). Hence, this study aimed to assess the level of knowledge and awareness of AI among faculty members and medicine students in one of the premier medical colleges in KSA. Methods A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023. Result A total of 131 participants contributed to our study, of which three were excluded due to incomplete responses, thereby giving a response rate of 98%. Conclusion 85.4% of the respondents believe that AI has a positive impact on the healthcare system and physicians in general. Hence, there should be a mandatory course in medical schools that can prepare future doctors to diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments.
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Affiliation(s)
- Asim M Alshanberi
- Department of Community Medicine and Pilgrims Health Care, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ahmed H Mousa
- Department of Neurosurgery, Graduate Medical Education, Mohammed Bin Rashid University (MBRU), Dubai Health, Dubai, United Arab Emirates
- Department of Neurosurgery, Rashid Hospital, Dubai Health, Dubai, United Arab Emirates
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sama A Hashim
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Reem S Almutairi
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sara Alrehali
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Aisha M Hamisu
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Mohammed Shaikhomer
- Department of Internal Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shakeel A Ansari
- Department of Biochemistry, General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
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Loutati R, Perel N, Marmor D, Maller T, Taha L, Amsalem I, Hitter R, Mohammed M, Levi N, Shrem M, Amro M, Shuvy M, Glikson M, Asher E. Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group. Front Cardiovasc Med 2024; 11:1333252. [PMID: 38500758 PMCID: PMC10944920 DOI: 10.3389/fcvm.2024.1333252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/21/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Despite ongoing efforts to minimize sex bias in diagnosis and treatment of acute coronary syndrome (ACS), data still shows outcomes differences between sexes including higher risk of all-cause mortality rate among females. Hence, the aim of the current study was to examine sex differences in ACS in-hospital mortality, and to implement artificial intelligence (AI) models for prediction of in-hospital mortality among females with ACS. Methods All ACS patients admitted to a tertiary care center intensive cardiac care unit (ICCU) between July 2019 and July 2023 were prospectively enrolled. The primary outcome was in-hospital mortality. Three prediction algorithms, including gradient boosting classifier (GBC) random forest classifier (RFC), and logistic regression (LR) were used to develop and validate prediction models for in-hospital mortality among females with ACS, using only available features at presentation. Results A total of 2,346 ACS patients with a median age of 64 (IQR: 56-74) were included. Of them, 453 (19.3%) were female. Female patients had higher prevalence of NSTEMI (49.2% vs. 39.8%, p < 0.001), less urgent PCI (<2 h) rates (40.2% vs. 50.6%, p < 0.001), and more complications during admission (17.7% vs. 12.3%, p = 0.01). In-hospital mortality occurred in 58 (2.5%) patients [21/453 (5%) females vs. 37/1,893 (2%) males, HR = 2.28, 95% CI: 1.33-3.91, p = 0.003]. GBC algorithm outscored the RFC and LR models, with area under receiver operating characteristic curve (AUROC) of 0.91 with proposed working point of 83.3% sensitivity and 82.4% specificity, and area under precision recall curve (AUPRC) of 0.92. Analysis of feature importance indicated that older age, STEMI, and inflammatory markers were the most important contributing variables. Conclusions Mortality and complications rates among females with ACS are significantly higher than in males. Machine learning algorithms for prediction of ACS outcomes among females can be used to help mitigate sex bias.
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Affiliation(s)
- Ranel Loutati
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
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Palermi S, Vecchiato M, Saglietto A, Niederseer D, Oxborough D, Ortega-Martorell S, Olier I, Castelletti S, Baggish A, Maffessanti F, Biffi A, D'Andrea A, Zorzi A, Cavarretta E, D'Ascenzi F. Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart? Eur J Prev Cardiol 2024; 31:470-482. [PMID: 38198776 DOI: 10.1093/eurjpc/zwae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 01/01/2024] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.
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Affiliation(s)
- Stefano Palermi
- Public Health Department, University of Naples Federico II, via Pansini 5, 80131 Naples, Italy
| | - Marco Vecchiato
- Sports and Exercise Medicine Division, Department of Medicine, University of Padova, 35128 Padova, Italy
| | - Andrea Saglietto
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Citta della Salute e della Scienza' Hospital, 10129 Turin, Italy
- Department of Medical Sciences, University of Turin, 10129 Turin, Italy
| | - David Niederseer
- Department of Cardiology, University Heart Center Zurich, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - David Oxborough
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Silvia Castelletti
- Cardiology Department, Istituto Auxologico Italiano IRCCS, 20149 Milan, Italy
| | - Aaron Baggish
- Cardiovascular Performance Program, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Alessandro Biffi
- Med-Ex, Medicine & Exercise, Medical Partner Scuderia Ferrari, 00187 Rome, Italy
| | - Antonello D'Andrea
- Department of Cardiology, Umberto I Hospital, 84014 Nocera Inferiore, Italy
| | - Alessandro Zorzi
- Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
| | - Elena Cavarretta
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Naples, Italy
| | - Flavio D'Ascenzi
- Department of Medical Biotechnologies, Division of Cardiology, University of Siena, 53100 Siena, Italy
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Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Aleksandra S, Robert K, Klaudia K, Dawid L, Mariusz S. Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2024; 12:e22. [PMID: 38572221 PMCID: PMC10988184 DOI: 10.22037/aaem.v12i1.2110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Introduction The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field. Methods This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review. Results Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible. Conclusions Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery.
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Affiliation(s)
- Szymczyk Aleksandra
- Department of Emergency Medicine, Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdansk, Poland
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Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
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Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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Cinteza E, Vasile CM, Busnatu S, Armat I, Spinu AD, Vatasescu R, Duica G, Nicolescu A. Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children? Diagnostics (Basel) 2024; 14:132. [PMID: 38248009 PMCID: PMC10814919 DOI: 10.3390/diagnostics14020132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/26/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike.
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Affiliation(s)
- Eliza Cinteza
- Department of Pediatrics, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (E.C.)
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Corina Maria Vasile
- Department of Pediatric and Adult Congenital Cardiology, University Hospital of Bordeaux, F-33600 Bordeaux, France;
| | - Stefan Busnatu
- Cardio-Thoracic Department, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Cardiology Department, “Prof. Dr. Bagdasar Arseni” Clinical Hospital, 041915 Bucharest, Romania
| | - Ionel Armat
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Arsenie Dan Spinu
- “Dr. Carol Davila” Central Emergency University Military Hospital, 010825 Bucharest, Romania;
- Department 3, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Radu Vatasescu
- Cardio-Thoracic Department, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Emergency Clinical Hospital, 014461 Bucharest, Romania
| | - Gabriela Duica
- Department of Pediatrics, Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (E.C.)
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
| | - Alin Nicolescu
- Pediatric Cardiology Department, “Marie Skolodowska Curie” Emergency Children’s Hospital, 041451 Bucharest, Romania; (I.A.); (A.N.)
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15
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Harris JE. An AI-Enhanced Electronic Health Record Could Boost Primary Care Productivity. JAMA 2023; 330:801-802. [PMID: 37548970 DOI: 10.1001/jama.2023.14525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
This Viewpoint discusses ways that artificial intelligence (AI) may improve the productivity of primary care physicians with easier and more accurate use of AI-enhanced electronic health records.
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Affiliation(s)
- Jeffrey E Harris
- Adult Medicine Department, Eisner Health, Los Angeles, California
- Department of Economics, Massachusetts Institute of Technology, Cambridge
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Venkatesh KP, Raza M, Kvedar J. AI-based skin cancer detection: the balance between access and overutilization. NPJ Digit Med 2023; 6:147. [PMID: 37582856 PMCID: PMC10427637 DOI: 10.1038/s41746-023-00900-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
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17
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Mohsin SN, Gapizov A, Ekhator C, Ain NU, Ahmad S, Khan M, Barker C, Hussain M, Malineni J, Ramadhan A, Halappa Nagaraj R. The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases. Cureus 2023; 15:e44374. [PMID: 37664359 PMCID: PMC10469091 DOI: 10.7759/cureus.44374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.
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Affiliation(s)
| | | | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, USA
| | - Noor U Ain
- Medicine, Mayo Hospital, Lahore, PAK
- Medicine, King Edward Medical University, Lahore, PAK
| | | | - Mavra Khan
- Medicine and Surgery, Mayo Hospital, Lahore , PAK
| | - Chad Barker
- Public Health, University of South Florida, Tampa, USA
| | | | - Jahnavi Malineni
- Medicine and Surgery, Maharajah's Institute of Medical Sciences, Vizianagaram, IND
| | - Afif Ramadhan
- Medicine, Universal Scientific Education and Research Network (USERN), Yogyakarta, IDN
- Medicine, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, IDN
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Wang ZY, Guo ZH. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chin J Integr Med 2023:10.1007/s11655-023-3639-7. [PMID: 37222830 DOI: 10.1007/s11655-023-3639-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 05/25/2023]
Abstract
High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
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Affiliation(s)
- Zi-Yan Wang
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Zhi-Hua Guo
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China.
- Hunan Key Laboratory of Colleges and Universities of Intelligent Traditional Chinese Medicine Diagnosis and Preventive Treatment of Chronic Diseases of Hunan Universities of Chinese Medicine, Changsha, 410208, China.
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