1
|
Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [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: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
Collapse
Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Abteilung AI4Health, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
| |
Collapse
|
3
|
Staszak K, Tylkowski B, Staszak M. From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4605. [PMID: 36901614 PMCID: PMC10002005 DOI: 10.3390/ijerph20054605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.
Collapse
Affiliation(s)
- Katarzyna Staszak
- Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| | - Bartosz Tylkowski
- Eurecat, Centre Tecnològic de Catalunya, C/Marcellí Domingo s/n, 43007 Tarragona, Spain
| | - Maciej Staszak
- Institute of Chemical Technology and Engineering, Faculty of Chemical Technology, Poznan University of Technology, ul. Berdychowo 4, 60-965 Poznan, Poland
| |
Collapse
|
4
|
Brandes A, Stavrakis S, Freedman B, Antoniou S, Boriani G, Camm AJ, Chow CK, Ding E, Engdahl J, Gibson MM, Golovchiner G, Glotzer T, Guo Y, Healey JS, Hills MT, Johnson L, Lip GYH, Lobban T, Macfarlane PW, Marcus GM, McManus DD, Neubeck L, Orchard J, Perez MV, Schnabel RB, Smyth B, Steinhubl S, Turakhia MP. Consumer-Led Screening for Atrial Fibrillation: Frontier Review of the AF-SCREEN International Collaboration. Circulation 2022; 146:1461-1474. [PMID: 36343103 PMCID: PMC9673231 DOI: 10.1161/circulationaha.121.058911] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/22/2022] [Indexed: 11/09/2022]
Abstract
The technological evolution and widespread availability of wearables and handheld ECG devices capable of screening for atrial fibrillation (AF), and their promotion directly to consumers, has focused attention of health care professionals and patient organizations on consumer-led AF screening. In this Frontiers review, members of the AF-SCREEN International Collaboration provide a critical appraisal of this rapidly evolving field to increase awareness of the complexities and uncertainties surrounding consumer-led AF screening. Although there are numerous commercially available devices directly marketed to consumers for AF monitoring and identification of unrecognized AF, health care professional-led randomized controlled studies using multiple ECG recordings or continuous ECG monitoring to detect AF have failed to demonstrate a significant reduction in stroke. Although it remains uncertain if consumer-led AF screening reduces stroke, it could increase early diagnosis of AF and facilitate an integrated approach, including appropriate anticoagulation, rate or rhythm management, and risk factor modification to reduce complications. Companies marketing AF screening devices should report the accuracy and performance of their products in high- and low-risk populations and avoid claims about clinical outcomes unless improvement is demonstrated in randomized clinical trials. Generally, the diagnostic yield of AF screening increases with the number, duration, and temporal dispersion of screening sessions, but the prognostic importance may be less than for AF detected by single-time point screening, which is largely permanent, persistent, or high-burden paroxysmal AF. Consumer-initiated ECG recordings suggesting possible AF always require confirmation by a health care professional experienced in ECG reading, whereas suspicion of AF on the basis of photoplethysmography must be confirmed with an ECG. Consumer-led AF screening is unlikely to be cost-effective for stroke prevention in the predominantly young, early adopters of this technology. Studies in older people at higher stroke risk are required to demonstrate both effectiveness and cost-effectiveness. The direct interaction between companies and consumers creates new regulatory gaps in relation to data privacy and the registration of consumer apps and devices. Although several barriers for optimal use of consumer-led screening exist, results of large, ongoing trials, powered to detect clinical outcomes, are required before health care professionals should support widespread adoption of consumer-led AF screening.
Collapse
Affiliation(s)
| | - Stavros Stavrakis
- Cardiovascular Section, University of Oklahoma Health Science Center
| | - Ben Freedman
- Heart Research Institute, University of Sydney, Sydney, Australia
| | | | - Giuseppe Boriani
- Department of Cardiology, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Clara K. Chow
- Cardiovascular Division, University of Sydney, Sydney, Australia
| | - Eric Ding
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
| | - Johan Engdahl
- Department of Cardiology, Karolinska Institute, Stockholm, Sweeden
| | - Michael M. Gibson
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | | | - Taya Glotzer
- Hackensack University Medical Center, Hackensack, NJ
| | - Yutao Guo
- Chinese PLA General Hospital, Beijing, China
| | | | | | | | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | | | | | - Gregory M. Marcus
- Department of Cardiology, University of California, San Francisco, San Franscisco, CA
| | - David D. McManus
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA
| | - Lis Neubeck
- Centre for Cardiovascular Health, Edinburgh Napier University
| | - Jessica Orchard
- Charles Perkins Centre, University of Sydney, Sydney, Australia
| | | | | | - Breda Smyth
- Department of Public Health, Health Service Executive West, Galway, Ireland
| | | | | |
Collapse
|