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Seetharam K, Thyagaturu H, Ferreira GL, Patel A, Patel C, Elahi A, Pachulski R, Shah J, Mir P, Thodimela A, Pala M, Thet Z, Hamirani Y. Broadening Perspectives of Artificial Intelligence in Echocardiography. Cardiol Ther 2024; 13:267-279. [PMID: 38703292 PMCID: PMC11093957 DOI: 10.1007/s40119-024-00368-3] [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: 11/13/2023] [Accepted: 04/11/2024] [Indexed: 05/06/2024] Open
Abstract
Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
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Affiliation(s)
- Karthik Seetharam
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
- Wyckoff Heights Medical Center, Brooklyn, NY, USA.
| | - Harshith Thyagaturu
- Division of Cardiovascular Disease, West Virgina University, Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | | | - Aditya Patel
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Chinmay Patel
- University of Pittsburg Medical Center, Harrisburg, PA, USA
| | - Asim Elahi
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Roman Pachulski
- St. John's Episcopal Hospital - South Shore, New York, NY, USA
| | - Jilan Shah
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Parvez Mir
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | | | - Manya Pala
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Zeyar Thet
- Wyckoff Heights Medical Center, Brooklyn, NY, USA
| | - Yasmin Hamirani
- Robert Woods Johnson University Hospital/Rutgers University, New Brusnwick, NJ, USA
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Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024:10.1007/s11883-024-01210-w. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [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: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
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Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
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Seringa J, Abreu J, Magalhaes T. Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol. BMJ Open 2024; 14:e083188. [PMID: 38580361 PMCID: PMC11002361 DOI: 10.1136/bmjopen-2023-083188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
INTRODUCTION Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic condition affecting over 64 million individuals globally.This scoping review aims to synthesise the evidence on ML methods, applications and economic analysis to predict the HF hospitalisation risk. METHODS AND ANALYSIS This scoping review will use the approach described by Arksey and O'Malley. This protocol will use the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol, and the PRISMA extension for scoping reviews will be used to present the results. PubMed, Scopus and Web of Science are the databases that will be searched. Two reviewers will independently screen the full-text studies for inclusion and extract the data. All the studies focusing on ML models to predict the risk of hospitalisation from HF adult patients will be included. ETHICS AND DISSEMINATION Ethical approval is not required for this review. The dissemination strategy includes peer-reviewed publications, conference presentations and dissemination to relevant stakeholders.
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Affiliation(s)
- Joana Seringa
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
| | - João Abreu
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
| | - Teresa Magalhaes
- NOVA National School of Public Health, NOVA University Lisbon, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
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Rouhi AD, Ghanem YK, Yolchieva L, Saleh Z, Joshi H, Moccia MC, Suarez-Pierre A, Han JJ. Can Artificial Intelligence Improve the Readability of Patient Education Materials on Aortic Stenosis? A Pilot Study. Cardiol Ther 2024; 13:137-147. [PMID: 38194058 PMCID: PMC10899139 DOI: 10.1007/s40119-023-00347-0] [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: 11/05/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024] Open
Abstract
INTRODUCTION The advent of generative artificial intelligence (AI) dialogue platforms and large language models (LLMs) may help facilitate ongoing efforts to improve health literacy. Additionally, recent studies have highlighted inadequate health literacy among patients with cardiac disease. The aim of the present study was to ascertain whether two freely available generative AI dialogue platforms could rewrite online aortic stenosis (AS) patient education materials (PEMs) to meet recommended reading skill levels for the public. METHODS Online PEMs were gathered from a professional cardiothoracic surgical society and academic institutions in the USA. PEMs were then inputted into two AI-powered LLMs, ChatGPT-3.5 and Bard, with the prompt "translate to 5th-grade reading level". Readability of PEMs before and after AI conversion was measured using the validated Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook Index (SMOGI), and Gunning-Fog Index (GFI) scores. RESULTS Overall, 21 PEMs on AS were gathered. Original readability measures indicated difficult readability at the 10th-12th grade reading level. ChatGPT-3.5 successfully improved readability across all four measures (p < 0.001) to the approximately 6th-7th grade reading level. Bard successfully improved readability across all measures (p < 0.001) except for SMOGI (p = 0.729) to the approximately 8th-9th grade level. Neither platform generated PEMs written below the recommended 6th-grade reading level. ChatGPT-3.5 demonstrated significantly more favorable post-conversion readability scores, percentage change in readability scores, and conversion time compared to Bard (all p < 0.001). CONCLUSION AI dialogue platforms can enhance the readability of PEMs for patients with AS but may not fully meet recommended reading skill levels, highlighting potential tools to help strengthen cardiac health literacy in the future.
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Affiliation(s)
- Armaun D Rouhi
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yazid K Ghanem
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Laman Yolchieva
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Zena Saleh
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Hansa Joshi
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | - Matthew C Moccia
- Department of Surgery, Cooper University Hospital, Camden, NJ, USA
| | | | - Jason J Han
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
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Bahlke F, Englert F, Popa M, Bourier F, Reents T, Lennerz C, Kraft H, Martinez AT, Kottmaier M, Syväri J, Tydecks M, Telishevska M, Lengauer S, Hessling G, Deisenhofer I, Erhard N. First clinical data on artificial intelligence-guided catheter ablation in long-standing persistent atrial fibrillation. J Cardiovasc Electrophysiol 2024; 35:406-414. [PMID: 38197476 DOI: 10.1111/jce.16184] [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: 09/11/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024]
Abstract
INTRODUCTION Despite advanced ablation strategies and major technological improvements, treatment of persistent atrial fibrillation (AF) remains challenging and the underlying pathophysiology is not fully understood. This study analyzed the multiple procedure outcome and safety of catheter ablation of spatiotemporal dispersions (DISPERS) detected by artificial intelligence (AI)-guided software in patients with long-standing persistent AF. METHODS AND RESULTS The Volta VX1 software was used for 50 consecutive patients undergoing catheter ablation for persistent AF. First, high-density mapping (78% biatrial) with a multipolar mapping catheter was performed. In addition to pulmonary vein isolation (PVI), ablation of DISPERS was performed aiming at homogenizing, dissecting, isolating, or connecting DISPERS areas to nonconducting anatomical structures. Follow-up contained regular visits at our outpatient clinic at 1, 3, 6, and 12 months including 7-day Holter electrocardiograms. Patients were mainly suffering from long-standing persistent AF (mean AF duration 50.30 ± 54.28 months). Following PVI, ablation of left atrial and right atrial DISPERS areas led to AF cycle length prolongation (mean of 162.0 ± 16.6 to 202.2 ± 21.6 ms after) and AF termination to atrial tachycardia (AT) or sinus rhythm (SR) in 12 patients (24%). No stroke or pericardial effusion occurred; major groin complications (pseudoaneurysm n = 1, atrioventricular fistula n = 1) were detected in two patients. After a blanking period of 6 weeks, recurrence of any atrial arrhythmia was documented in 26 patients (52%). The majority of patients presented with organized AT (n = 15) while AF was present in n = 9 patients and AT/AF was observed in n = 2 patients. Twenty-two patients underwent reablation. During a mean follow-up of 363.14 ± 187.42 days and after an average of 1.46 ± 0.68 procedures, 82% of patients remained in stable SR. CONCLUSION DISPERS-guided ablation using machine learning software (the Volta VX1 software) in addition to PVI in long-standing persistent AF ablation resulted in high long-term success rates regarding AF and AT elimination. Most arrhythmia recurrences were reentrant AT. After a total of 1.46 ± 0.68 procedures, freedom from AF/AT was 82%. Despite prolonged procedure times complication rates were low. Randomized studies are necessary to evaluate long-term efficacy of dispersion-guided ablation using AI.
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Affiliation(s)
- Fabian Bahlke
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Florian Englert
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Miruna Popa
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Felix Bourier
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Tilko Reents
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Carsten Lennerz
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Hannah Kraft
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Alex Tunsch Martinez
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Marc Kottmaier
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Jan Syväri
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Madeleine Tydecks
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Marta Telishevska
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Sarah Lengauer
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Gabriele Hessling
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Isabel Deisenhofer
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
| | - Nico Erhard
- Department of Electrophysiology, German Heart Center Munich, Technical University of Munich, Munich, Germany
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Escribano P, Ródenas J, García M, Arias MA, Hidalgo VM, Calero S, Rieta JJ, Alcaraz R. Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation. Heliyon 2024; 10:e25295. [PMID: 38327415 PMCID: PMC10847938 DOI: 10.1016/j.heliyon.2024.e25295] [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: 03/13/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/09/2024] Open
Abstract
Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF. Beyond introducing new indices to characterize the fibrillatory waves (f-waves) recorded through the preoperative electrocardiogram (ECG), the aim of this study is to combine frequency- and time-domain features to improve CA outcome prediction and optimize patient selection for the procedure, given the absence of any study that jointly analyzes information from both domains. Precisely, the f-waves of 151 persistent AF patients undergoing their first CA procedure were extracted from standard V1 lead. Novel spectral and amplitude features were derived from these waves and combined through a machine learning algorithm to anticipate the intervention mid-term outcome. The power rate index (φ), which estimates the power of the harmonic content regarding the dominant frequency (DF), yielded the maximum individual discriminant ability of 64% to discern between individuals who experienced a recurrence of AF and those who sustained SR after a 9-month follow-up period. The predictive accuracy was improved up to 78.5% when this parameter φ was merged with the amplitude spectrum area in the DF bandwidth (A M S A L F ) and the normalized amplitude of the f-waves into a prediction model based on an ensemble classifier, built by random undersampling boosting of decision trees. This outcome suggests that the synthesis of both spectral and temporal features of the f-waves before CA might enrich the prognostic knowledge of this therapy for persistent AF patients.
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Affiliation(s)
- Pilar Escribano
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain
| | - Juan Ródenas
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain
| | - Manuel García
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain
| | - Miguel A. Arias
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Toledo, Toledo, Spain
| | - Víctor M. Hidalgo
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - Sofía Calero
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, Valencia, Spain
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain
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Galimzhanov A, Matetic A, Tenekecioglu E, Mamas MA. Prediction of clinical outcomes after percutaneous coronary intervention: Machine-learning analysis of the National Inpatient Sample. Int J Cardiol 2023; 392:131339. [PMID: 37678434 DOI: 10.1016/j.ijcard.2023.131339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/08/2023] [Accepted: 09/03/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND This study aimed to develop a multiclass machine-learning (ML) model to predict all-cause mortality, ischemic and hemorrhagic events in unselected hospitalized patients undergoing percutaneous coronary intervention (PCI). METHODS This retrospective study included 1,815,595 unselected weighted hospitalizations undergoing PCI from the National Inpatient Sample (2016-2019). Five most common ML algorithms (logistic regression, support vector machine (SVM), naive Bayes, random forest (RF), and extreme gradient boosting (XGBoost)) were trained and tested with 101 input features. The study endpoints were different combinations of all-cause mortality, ischemic cerebrovascular events (CVE) and major bleeding. An area under the curve (AUC) with 95% confidence interval (95% CI) was selected as a performance metric. RESULTS The study population was split to a training cohort of 1,186,880 PCI discharges, validation cohort (for calibration) of 296,725 hospitalizations and a test cohort of 331,990 PCI discharges. A total of 98,180 (5.4%) hospital entries included study outcomes. Logistic regression, SVM, naive Bayes, and RF model demonstrated AUCs of 0.83 (95% CI 0.82-0.84), 0.84 (95% CI 0.83-0.86), 0.81 (95% CI 0.80-0.82), and 0.83 (95% CI 0.81-0.84), retrospectively. The XGBoost classifier performed the best with an AUC of 0.86 (95% CI 0.85-0.87) with excellent calibration. We then built a web-based application that provides predictions based on the XGBoost model. CONCLUSION We derived the multi-task XGBoost classifier based on 101 features to predict different combinations of all-cause death, ischemic CVE and major bleeding. Such models may be useful in benchmarking and risk prediction using routinely collected administrative data.
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Affiliation(s)
- Akhmetzhan Galimzhanov
- Department of Propedeutics of Internal Disease, Semey Medical University, Semey, Kazakhstan; Keele Cardiovascular Research Group, Keele University, Keele, UK.
| | - Andrija Matetic
- Keele Cardiovascular Research Group, Keele University, Keele, UK; Department of Cardiology, University Hospital of Split, Split 21000, Croatia
| | - Erhan Tenekecioglu
- Department of Cardiology, Bursa Education and Research Hospital, Health Sciences University, Bursa,Turkey; Department of Cardiology, Thoraxcenter, Erasmus MC, Erasmus University, Rotterdam, the Netherlands
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Keele, UK
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Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
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Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
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