Artificial Intelligence Applications in Cardiology
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Invited Paper
P: 41-45
March 2022

Artificial Intelligence Applications in Cardiology

J Ankara Univ Fac Med 2022;75(1):41-45
1. Ankara Üniversitesi Tıp Fakültesi, Kardiyoloji Anabilim Dalı, Ankara, Türkiye
No information available.
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Received Date: 11.11.2022
Accepted Date: 23.11.2022
Publish Date: 18.01.2023
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ABSTRACT

Cardiology is one of the medical specialties that uses technology on a large scale for routine clinical care. Artificial intelligence can be applied in cardiological care in several ways such as: Predicting of various diseases by analysis of signals from 12-lead ECG and wearable-portable devices; detecting arrhythmias, especially atrial fibrillation; obtaining and processing the appropriate image during imaging examinations, making a diagnosis by taking measurements; and guiding personalized medicine practices with diagnosis through voice and natural language analysis, clinical risk determination algorithms or phenotyping. Large-scale studies involving clinicians are essential in order to evaluate these applications, which have not yet entered routine clinical practice.

Keywords: Artificial Intelligence, Machine Learning, Arrythmia Detection, ECG Analysis, Wearable Devices

References

1
Quer G, Arnaout R, Henne M, et al. Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review. J Am Coll Cardiol. 2021;77:300-313.
2
Van den Eynde J, Lachmann M, Laugwitz KL, et al. Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends Cardiovasc Med. 2022:S1050-1738(22)00012-3.
3
Reichlin T, Abächerli R, Twerenbold R, et al. Advanced ECG in 2016: is there more than just a tracing? Swiss Med Wkly. 2016;146:w14303.
4
Hongo RH, Goldschlager N. Status of computerized electrocardiography. Cardiol Clin. 2006;24:491-504.
5
Guglin ME, Thatai D. Common errors in computer electrocardiogram interpretation. Int J Cardiol. 2006;106:232-237.
6
Schläpfer J, Wellens HJ. Computer-Interpreted Electrocardiograms: Benefits and Limitations. J Am Coll Cardiol. 2017;70:1183-1192.
7
Chang KC, Hsieh PH, Wu MY, et al. Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms. Can J Cardiol. 2021;37:94-104.
8
Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25:65-69.
9
Galloway CD, Valys AV, Shreibati JB, et al. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 2019;4:428-436.
10
Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25:70-74.
11
Kwon JM, Lee SY, Jeon KH, et al. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020;9:e014717.
12
Elias P, Poterucha TJ, Rajaram V, et al. Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease. J Am Coll Cardiol. 2022;80:613-626.
13
Lee Y, Choi B, Lee MS, et al. An artificial intelligence electrocardiogram analysis for detecting cardiomyopathy in the peripartum period. Int J Cardiol. 2022;352:72-77.
14
Gladstone DJ, Spring M, Dorian P, et al. Atrial fibrillation in patients with cryptogenic stroke. N Engl J Med. 2014;370:2467-2477.
15
Lindow 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;37:426-433.
16
Attia ZI, Noseworthy PA, Lopez-Jimenez F, 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;394:861-867.
17
Noseworthy PA, Attia ZI, Behnken EM, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet. 2022;400:1206-1212.
18
Sehrawat O, Kashou AH, Noseworthy PA. Artificial intelligence and atrial fibrillation. J Cardiovasc Electrophysiol. 2022;33:1932-1943.
19
Guo Y, Wang H, Zhang H, et al. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol. 2019;74:2365-2375.
20
Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381:1909-1917.
21
Bumgarner JM, Lambert CT, Hussein AA, et al. Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. J Am Coll Cardiol. 2018;71:2381-2388.
22
Kusunose K. Steps to use artificial intelligence in echocardiography. J Echocardiogr. 2021;19:21-27.
23
Voelker R. Cardiac Ultrasound Uses Artificial Intelligence to Produce Images. JAMA. 2020;323:1034.
24
Narang A, Bae R, Hong H, et al. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. 2021;6:624-632.
25
Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2018;1:6.
26
Zhang J, Gajjala S, Agrawal P, et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018;138:1623-1635.
27
D. O. EchoNet-RCT: Safety and Efficacy Study of AI LVEF. Presented at: ESC 2022 August 27, 2022 Barcelona, Spain. 2022.
28
Narula S, Shameer K, Salem Omar AM, et al. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol. 2016;68:2287-2295.
29
Sengupta PP, Huang YM, Bansal M, et al. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging. 2016;9:e004330.
30
Jeganathan J, Knio Z, Amador Y, et al. Artificial intelligence in mitral valve analysis. Ann Card Anaesth. 2017;20:129-134.
31
Coenen A, Kim YH, Kruk M, et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium. Circ Cardiovasc Imaging. 2018;11:e007217.
32
Gupta MD, Kunal S, Girish MP, et al. Artificial intelligence in cardiology: The past, present and future. Indian Heart J. 2022;74:265-269.
33
Kalscheur MM, Kipp RT, Tattersall MC, et al. Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circ Arrhythm Electrophysiol. 2018;11:e005499.
34
Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131:269-279.
35
Nahar JK, Lopez-Jimenez F. Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care. Heart Fail Clin. 2022;18:311-323.
36
Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21:74-85.
37
Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin. 2022;18:245-258.
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