INVITED REVIEW
Artificial intelligence and atrial fibrillation
Ojasav Sehrawat MBBS,
Anthony H. Kashou MD,
Peter A. Noseworthy MD,
Ojasav Sehrawat MBBS
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Search for more papers by this authorAnthony H. Kashou MD
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Search for more papers by this authorCorresponding Author
Peter A. Noseworthy MD
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Correspondence: Peter A. Noseworthy, Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW,Rochester, MN 55905, USA.
Email: [email protected]
Search for more papers by this authorOjasav Sehrawat MBBS,
Anthony H. Kashou MD,
Peter A. Noseworthy MD,
Ojasav Sehrawat MBBS
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Search for more papers by this authorAnthony H. Kashou MD
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Search for more papers by this authorCorresponding Author
Peter A. Noseworthy MD
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Correspondence: Peter A. Noseworthy, Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW,Rochester, MN 55905, USA.
Email: [email protected]
Search for more papers by this authorDisclosures: None
Abstract
Background
In the context of atrial fibrillation (AF), traditional clinical practices have thus fallen short in several domains, such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems.
Aims
To discuss the roles of artificial intelligence (AI)-enabled electrocardiogram (ECG) pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models.
Materials & Methods
An extensive search and review of the currently available literature on the topics.
Results
One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Challenges with regards to the benefits and harms of AF screening remain. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm.
Discussion
Knowledge gaps remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and identifying those who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. The role of DL models assessing AF burden from long-duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, the role of adequate external validation of the models and clinical trials to study true performance is discussed.
Conclusion
Algorithms using AI to interpret ECGs in various new ways have been developed. While still, much work needs to be done, these technologies have shown enormous potential in a short span of time. With further advancements and continuous research, these novel ways of interpretation may well become part of everyday clinical workflow.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
REFERENCES
- 1Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med. 2014; 370(26): 2478-2486.
- 2Sanna T, Diener HC, Passman RS, et al. Guidelines for electrocardiography. A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Committee on Electrocardiography). Circulation. 1992; 85(3): 1221-1228.
- 3Hornick J, Costantini O. The electrocardiogram: still a useful tool in the primary care office. Med Clin North Am. 2019; 103(5): 775-784.
- 4Hornick J, Costantini O. Assessment of the 12-lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12-25 years of age): a scientific statement from the American Heart Association and the American College of Cardiology. Circulation. 2014; 130(15): 1303-1334.
- 5Garvey JL, Zegre-Hemsey J, Gregg R, Studnek JR. Electrocardiographic diagnosis of ST segment elevation myocardial infarction: an evaluation of three automated interpretation algorithms. J Electrocardiol. 2016; 49(5): 728-732.
- 6Garvey JL, Zegre-Hemsey J, Gregg R, Studnek JR. Utility of the prehospital electrocardiogram in diagnosing acute coronary syndromes: the Myocardial Infarction Triage and Intervention (MITI) Project. J Am Coll Cardiol. 1998; 32(1): 17-27.
- 7Kudenchuk PJ, Maynard C, Cobb LA, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019; 25(1): 65-69.
- 8Hannun AY, Rajpurkar P, Haghpanahi M, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020; 11(1): 1760-1769.
- 9Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021; 18(7): 465-478.
- 10Kashou A, Mulpuru SK, Deshmukh AJ, et al. An artificial intelligence–enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the ‘Turing test’? Cardiovasc Dig Health J. 2021; 2(3): 164-170.
- 11Attia ZI, Friedman PA, Noseworthy PA, et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ Arrhythm Electrophysiol. 2019; 12(9):e007284.
- 12Attia ZI, Friedman PA, Noseworthy PA, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019; 394(10201): 861-867.
- 13Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol. 2020; 75(7): 722-733.
- 14Guglin ME, Thatai D. Common errors in computer electrocardiogram interpretation. Int J Cardiol. 2006; 106(2): 232-237.
- 15Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020; 9(2): 14-237.
- 16O'Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv. 2015: 151108458.
- 17Patel NJ, Deshmukh A, Pant S, et al. Contemporary trends of hospitalization for atrial fibrillation in the United States, 2000 through 2010: implications for healthcare planning. Circulation. 2014; 129(23): 2371-2379.
- 18Chugh SS, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation. 2014; 129(8): 837-847.
- 19Samuel M, Brophy JM. Challenges in assessing the incidence of atrial fibrillation hospitalizations. Can J Cardiol. 2019; 35(10): 1291-1293.
- 20Coyne KS, Paramore C, Grandy S, Mercader M, Reynolds M, Zimetbaum P. Assessing the direct costs of treating nonvalvular atrial fibrillation in the United States. Value Health. 2006; 9(5): 348-356.
- 21Lloyd-Jones DM, Wang TJ, Leip EP, et al. Lifetime risk for development of atrial fibrillation: the Framingham Heart Study. Circulation. 2004; 110(9): 1042-1046.
- 22Go AS, Hylek EM, Phillips KA, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001; 285(18): 2370-2375.
- 23Lip GYH, Brechin CM, Lane DA. The global burden of atrial fibrillation and stroke: a systematic review of the epidemiology of atrial fibrillation in regions outside North America and Europe. Chest. 2012; 142(6): 1489-1498.
- 24Morin DP, Bernard ML, Madias C, Rogers PA, Thihalolipavan S, Estes NA. The state of the art: atrial fibrillation epidemiology, prevention, and treatment. Mayo Clin Proc. 2016; 91(12): 1778-1810.
- 25Yuan S, Larsson SC. No association between coffee consumption and risk of atrial fibrillation: a Mendelian randomization study. Nutr Metab Cardiovasc Dis. 2019; 29(11): 1185-1188. doi:10.1016/j.numecd.2019.07.015
- 26Stewart S, Hart CL, Hole DJ, McMurray JJ. A population-based study of the long-term risks associated with atrial fibrillation: 20-year follow-up of the Renfrew/Paisley study. Am J Med. 2002; 113(5): 359-364.
- 27Hongo RH, Goldschlager N. Overreliance on computerized algorithms to interpret electrocardiograms. Am J Med. 2004; 117(9): 706-708.
- 28Shah AP, Rubin SA. Errors in the computerized electrocardiogram interpretation of cardiac rhythm. J Electrocardiol. 2007; 40(5): 385-390.
- 29Bogun F, Anh D, Kalahasty G, et al. Misdiagnosis of atrial fibrillation and its clinical consequences. Am J Med. 2004; 117(9): 636-642.
- 30Lindow T, Kron J, Thulesius H, Ljungström E, Pahlm O. Erroneous computer-based interpretations of atrial fibrillation and atrial flutter in a Swedish primary health care setting. Scand J Prim Health Care. 2019; 37(4): 426-433.
- 31Mant J, Fitzmaurice DA, Hobbs FDR, et al. Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial. BMJ. 2007; 335(7616): 380.
- 32Schläpfer J, Wellens HJ. Computer-interpreted electrocardiograms: benefits and limitations. J Am Coll Cardiol. 2017; 70(9): 1183-1192.
- 33Madias JE. Computerized interpretation of electrocardiograms: taking stock and implementing new knowledge. J Electrocardiol. 2018; 51(3): 413-415.
- 34Novotny T, Bond R, Andrsova I, et al. The role of computerized diagnostic proposals in the interpretation of the 12-lead electrocardiogram by cardiology and non-cardiology fellows. Int J Med Inform. 2017; 101: 85-92.
- 35Anh D, Krishnan S, Bogun F. Accuracy of electrocardiogram interpretation by cardiologists in the setting of incorrect computer analysis. J Electrocardiol. 2006; 39(3): 343-345.
- 36Clifford GD, Liu C, Moody B, et al. AF classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017. Comput Cardiol. 2010; 2017-44.
- 37Smith SW, Walsh B, Grauer K, et al. A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. J Electrocardiol. 2019; 52: 88-95.
- 38Koshau AH, Ko W-Y, Attia ZI, et al. A comprehensive artificial intelligence–enabled electrocardiogram interpretation program. Cardiovasc Dig Health J. 2020; 1(2): 62-70.
- 39Svennberg E, Friberg L, Frykman V, Al-Khalili F, Engdahl J, Rosenqvist M. Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): a multicentre, parallel group, unmasked, randomised controlled trial. Lancet. 2021; 398(10310): 1498-1506.
- 40Svennberg E, Engdahl J, Al-Khalili F, Friberg L, Frykman V, Rosenqvist M. Mass screening for untreated atrial fibrillation: the STROKESTOP study. Circulation. 2015; 131(25): 2176-2184.
- 41Svendsen JH, Diederichsen SZ, Højberg S, et al. Implantable loop recorder detection of atrial fibrillation to prevent stroke (The LOOP Study): a randomised controlled trial. Lancet. 2021; 398(10310): 1507-1516.
- 42Fitzmaurice DA, McCahon D, Baker J, et al. Is screening for AF worthwhile? Stroke risk in a screened population from the SAFE study. Fam Pract. 2014; 31(3): 298-302.
- 43Swancutt D, Hobbs R, Fitzmaurice D, et al. A randomised controlled trial and cost effectiveness study of systematic screening (targeted and total population screening) versus routine practice for the detection of atrial fibrillation in the over 65s: (SAFE) [ISRCTN19633732]. BMC Cardiovasc Disord. 2004; 4: 12.
- 44Menke J, Lüthje L, Kastrup A, Larsen J. Thromboembolism in atrial fibrillation. Am J Cardiol. 2010; 105(4): 502-510.
- 45Danias PG, Caulfield TA, Weigner MJ, Silverman DI, Manning WJ. Likelihood of spontaneous conversion of atrial fibrillation to sinus rhythm. J Am Coll Cardiol. 1998; 31(3): 588-592.
- 46Risk factors for stroke and efficacy of antithrombotic therapy in atrial fibrillation. Analysis of pooled data from five randomized controlled trials. Arch Intern Med. 1994; 154(13): 1449-1457.
- 47van Walraven C, Hart RG, Connolly S, et al. Effect of age on stroke prevention therapy in patients with atrial fibrillation: the atrial fibrillation investigators. Stroke. 2009; 40(4): 1410-1416.
- 48Tereshchenko LG, Henrikson CA, Cigarroa J, Steinberg JS. Comparative effectiveness of interventions for stroke prevention in atrial fibrillation: a network meta-analysis. J Am Heart Assoc. 2016; 5(5) .
- 49Romero JR, Morris J, Pikula A. Stroke prevention: modifying risk factors. Ther Adv Cardiovasc Dis. 2008; 2(4): 287-303.
- 50Jonas DE, Kahwati LC, Yun JDY, Middleton JC, Coker-Schwimmer M, Asher GN. Screening for atrial fibrillation with electrocardiography: evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018; 320(5): 485-498.
- 51Martínez-Sellés M, Massó-van Roessel A, Álvarez-García J, et al. Interatrial block and atrial arrhythmias in centenarians: Prevalence, associations, and clinical implications. Heart Rhythm. 2016; 13(3): 645-651.
- 52Arboix A, Martí L, Dorison S, Sánchez MJ. Bayés syndrome and acute cardioembolic ischemic stroke. World J Clin Cases. 2017; 5(3): 93-101.
- 53Rabinstein AA, Yost MD, Faust L, et al. Artificial intelligence-electrocardiography to predict incident atrial fibrillation: a population-based study. Circ Arrhythm Electrophysiol. 2020; 13(12):e009355.
- 54Alonso A, Roetker NS, Soliman EZ, Chen LY, Greenland P, Heckbert SR. Subclinical and device-detected atrial fibrillation: pondering the knowledge gap: a scientific statement from the American Heart Association. Circulation. 2019; 140(25): e944-e963.
- 55Schnabel RB, Sullivan LM, Levy D, et al. ArtificIal Intelligence-enabled ECG to identify silent atrial fibrillation in embolic stroke of unknown source. J Stroke Cerebrovasc Dis. 2021; 30(9): 105998-745.
- 56Alonso A, Roetker NS, Soliman EZ, Chen LY, Greenland P, Heckbert SR. Prediction of atrial fibrillation in a racially diverse cohort: the Multi-Ethnic Study of Atherosclerosis (MESA). J Am Heart Assoc. 2016; 5(2):000102.
- 57Christopoulos G, Graff-Radford J, Lopez CL, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009; 373(9665): 739-745.
- 58Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013; 2(2):e000102.
- 59Kashou AH, Rabinstein AA, Attia IZ, et al. Electrocardiogram-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation. 2021; 6: 202-205.
- 60Saver JL. Recurrent cryptogenic stroke: a potential role for an artificial intelligence-enabled electrocardiogram? HeartRhythm Case Rep. 2020; 6(4): 202-205.
- 61Saver JL. Clinical practice. Cryptogenic stroke. N Engl J Med. 2016; 374(21): 2065-2074.
- 62Sacco RL, Ellenberg JH, Mohr JP, et al. Incidence, outcome, risk factors, and long-term prognosis of cryptogenic transient ischaemic attack and ischaemic stroke: a population-based study. Lancet Neurol. 2015; 14(9): 903-913.
- 63Wolf ME, Grittner U, Bottcher T, Norrving B, Rolfs A, Hennerici MG. Infarcts of undetermined cause: the NINCDS Stroke Data Bank. Ann Neurol. 1989; 25(4): 382-390.
- 64Marini C, De Santis F, Sacco S, et al. Phenotypic ASCO characterisation of young patients with ischemic stroke in the prospective multicentre observational sifap1 study. Cerebrovasc Dis. 2015; 40(3-4): 129-135.
- 65Paciaroni M, Agnelli G, Caso V, et al. Contribution of atrial fibrillation to incidence and outcome of ischemic stroke: results from a population-based study. Stroke. 2005; 36(6): 1115-1119.
- 66Kernan WN, Ovbiagele B, Black HR, et al. Atrial fibrillation in patients with first-ever stroke: frequency, antithrombotic treatment before the event and effect on clinical outcome. J Thromb Haemost. 2005; 3(6): 1218-1223.
- 67Diener HC, Sacco RL, Easton JD, et al. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2014; 45(7): 2160-2236.
- 68Hart RG, Sharma M, Mundl H, et al. Dabigatran for prevention of stroke after embolic stroke of undetermined source. N Engl J Med. 2019; 380(20): 1906-1917.
- 69Hart RG, Connolly SJ, Mundl H. Rivaroxaban for stroke prevention after embolic stroke of undetermined source. N Engl J Med. 2018; 379(10): 987-214.
- 70Geisler T, Poli S, Meisner C, et al. The AtRial Cardiopathy and Antithrombotic Drugs In prevention After cryptogenic stroke randomized trial: rationale and methods. Int J Stroke. 2019; 14(2): 207-214.
- 71Yao X, Attia ZI, Behnken EM, et al. Apixaban for treatment of embolic stroke of undetermined source (ATTICUS randomized trial): rationale and study design. Int J Stroke. 2017; 12(9): 985-990.
- 72Yao X, Attia ZI, Behnken EM, et al. Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial. Am Heart J. 2021; 239: 73-79.
- 73Chen LY, Chung MK, Allen LA, et al. AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. Circulation. 2014; 130(23): e199-e267.
- 74Swiryn S, Orlov MV, Benditt DG, et al. Atrial fibrillation burden: moving beyond atrial fibrillation as a binary entity: a scientific statement from the American Heart Association. Circulation. 2018; 137(20): e623-e644.
- 75Rankin AJ, Tran RT, Abdul-Rahim AH, Rankin AC, Lees KR. Clinical implications of brief device-detected atrial tachyarrhythmias in a cardiac rhythm management device population: results from the registry of atrial tachycardia and atrial fibrillation episodes. Circulation. 2016; 134(16): 1130-1140.
- 76Rankin AJ, Tran RT, Abdul-Rahim AH, Rankin AC, Lees KR. Clinically important atrial arrhythmia and stroke risk: a UK-wide online survey among stroke physicians and cardiologists. QJM. 2014; 107(11): 895-902.
- 77Freedman B, Boriani G, Glotzer TV, Healey JS, Kirchhof P, Potpara TS. Management of atrial high-rate episodes detected by cardiac implanted electronic devices. Nat Rev Cardiol. 2017; 14(12): 701-714.
- 78Perino AC, Fan J, Askari M, et al. ACC expert consensus decision pathway for anticoagulant and antiplatelet therapy in patients with atrial fibrillation or venous thromboembolism undergoing percutaneous coronary intervention or with atherosclerotic cardiovascular disease: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2020; 77(5): 629-658.
- 79Noseworthy PA, Kaufman ES, Chen LY, et al. Practice variation in anticoagulation prescription and outcomes after device-detected atrial fibrillation. Circulation. 2019; 139(22): 2502-2512.
- 80Lopes RD, Alings M, Connolly SJ, et al. Rationale and design of the Apixaban for the Reduction of Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial Fibrillation (ARTESiA) trial. Am Heart J. 2017; 189: 137-145.
- 81Kirchhof P, Blank BF, Calvert M, et al. Probing oral anticoagulation in patients with atrial high rate episodes: Rationale and design of the Non-vitamin K antagonist Oral anticoagulants in patients with Atrial High rate episodes (NOAH-AFNET 6) trial. Am Heart J. 2017; 190: 12-18.
- 82Shashikumar SP, Shah AJ, Clifforfd GD, Nemati S. Detection of paroxysmal atrial fibrillation using attention-based bidirectional recurrent neural networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2018.
- 83Pereira T, Tran N, Gadhoumi K, et al. Photoplethysmography based atrial fibrillation detection: a review. NPJ Digit Med. 2020; 3: 3.
- 84Tison GH, Sanchez JM, Ballinger B, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol. 2018; 3(5): 409-416.
- 85Poh MZ, Poh YC, Chan PH, et al. Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms. Heart. 2018; 104(23): 1921-1928.
- 86Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol. 2019; 74(19): 2365-2375.
- 87Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019; 381(20): 1909-1917.
- 88Lubitz SA, Faranesh AZ, Atlas SJ, et al. Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study. Am Heart J. 2021; 238: 16-26.
- 89Bonomi AG, Schipper F, Eerikäinen LM, et al. Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist. J Am Heart Assoc. 2018; 7(15):e009351.
- 90Veronese G, Germini F, Ingrassia S, et al. Emergency physician accuracy in interpreting electrocardiograms with potential ST-segment elevation myocardial infarction: Is it enough? Acute Card Care. 2016; 18(1): 7-10.
- 91Yasin OZ, Attia Z, Dillon JJ, et al. Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone. J Electrocardiol. 2017; 50(5): 620-625.
- 92Kashou AH, May AM, Noseworthy PA. Artificial intelligence-enabled ECG: a modern lens on an old technology. Curr Cardiol Rep. 2020; 22(8): 57.
Citing Literature
