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Micali G, Corallo F, Pagano M, Giambò FM, Duca A, D’Aleo P, Anselmo A, Bramanti A, Garofano M, Mazzon E, Bramanti P, Cappadona I. Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice. Healthcare (Basel) 2024; 12:1380. [PMID: 39057522 PMCID: PMC11276532 DOI: 10.3390/healthcare12141380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management.
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Affiliation(s)
- Giuseppe Micali
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Francesco Corallo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Maria Pagano
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Fabio Mauro Giambò
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Antonio Duca
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Piercataldo D’Aleo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Anna Anselmo
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Marina Garofano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Emanuela Mazzon
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
| | - Placido Bramanti
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
- Faculty of Psychology, Università degli Studi eCampus, Via Isimbardi 10, 22060 Novedrate, Italy
| | - Irene Cappadona
- IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy; (G.M.)
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Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, Attia ZI. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:314-323. [PMID: 38774362 PMCID: PMC11104462 DOI: 10.1093/ehjdh/ztae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/24/2024]
Abstract
Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
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Affiliation(s)
- Kathryn E Mangold
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | | | - Peter A Noseworthy
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | | | - Samuel J Asirvatham
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
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Lopez-Jimenez F, Kapa S, Friedman PA, LeBrasseur NK, Klavetter E, Mangold KE, Attia ZI. Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series. JACC Clin Electrophysiol 2024; 10:775-789. [PMID: 38597855 DOI: 10.1016/j.jacep.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: 10/20/2023] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 04/11/2024]
Abstract
Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.
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Affiliation(s)
- Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Nathan K LeBrasseur
- Robert and Arlene Kogod Center on Aging, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Eric Klavetter
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Kathryn E Mangold
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
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Bennati E, Capponi G, Favilli S, Girolami F, Gozzini A, Spaziani G, Passantino S, Tamburini A, Tondo A, Olivotto I. Role of Genetic Testing for Cardiomyopathies in Pediatric Patients With Left Ventricular Dysfunction Secondary to Chemotherapy. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004353. [PMID: 38357805 DOI: 10.1161/circgen.123.004353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Affiliation(s)
- Elena Bennati
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Guglielmo Capponi
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Silvia Favilli
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Francesca Girolami
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Alessia Gozzini
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Gaia Spaziani
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Silvia Passantino
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Angela Tamburini
- Oncology Division (A. Tamburini, A. Tondo), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Annalisa Tondo
- Oncology Division (A. Tamburini, A. Tondo), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
| | - Iacopo Olivotto
- Cardiology Unit (E.B., G.C., S.F., F.G., A.G., G.S., S.P., I.O.), Meyer Children's Hospital Istituto di Ricovero e Cura a Carattere Scientifico, Florence, Italy
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Naser JA, Kane GC, Lopez-Jimenez F. A novel non-invasive estimate of biological age: can an echocardiogram measure the patient's age? Eur J Prev Cardiol 2024; 31:242-243. [PMID: 37758216 DOI: 10.1093/eurjpc/zwad307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Jwan A Naser
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Pignolo RJ. AI-ECG and the Prediction of Accelerated Aging. Mayo Clin Proc 2023; 98:502-503. [PMID: 37019510 DOI: 10.1016/j.mayocp.2023.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 02/20/2023] [Indexed: 04/07/2023]
Affiliation(s)
- Robert J Pignolo
- Divisions of Hospital Internal Medicine and Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine; Section on Geriatric Medicine and Gerontology; and Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN.
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