Ranka S, Reddy M, Noheria A. Artificial intelligence in cardiovascular medicine. Curr Opin Cardiol. 2021;36:26–35. https://doi.org/10.1097/HCO.0000000000000812.

Article  PubMed  Google Scholar 

Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023;10. https://doi.org/10.3390/jcdd10040175.

•• Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25:65–9. https://doi.org/10.1038/s41591-018-0268-3. These authors set afire the frenzy in AI-ECG interpretation with this seminal work on rhythm classification from ambulatory single-lead ECGs. This paper discusses the success of AI-ECG for rhythm classification, and is a benchmark for any new publications in this space.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381:1909–17. https://doi.org/10.1056/NEJMoa1901183.

Article  PubMed  PubMed Central  Google Scholar 

Mehta S, Avila J, Villagran C, Fernandez F, Niklitschek S, Vera F, Rocuant R, Cardenas G, Frauenfelder A, Vieira D, et al. Artificial intelligence methodology: multi-label classification of abnormal EKG records. Eur Heart J. 2020;41:3450–3450. https://doi.org/10.1093/ehjci/ehaa946.3450.

Article  Google Scholar 

Kashou AH, Ko WY, Attia ZI, Cohen MS, Friedman PA, Noseworthy PA. A comprehensive artificial intelligence-enabled electrocardiogram interpretation program. Cardiovasc Digit Health J. 2020;1:62–70. https://doi.org/10.1016/j.cvdhj.2020.08.005.

Article  PubMed  PubMed Central  Google Scholar 

Xiong Z, Nash MP, Cheng E, Fedorov VV, Stiles MK, Zhao J. ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol Meas. 2018;39:094006. https://doi.org/10.1088/1361-6579/aad9ed.

Article  PubMed  PubMed Central  Google Scholar 

Kamaleswaran R, Mahajan R, Akbilgic O. A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length. Physiol Meas. 2018;39:035006. https://doi.org/10.1088/1361-6579/aaaa9d.

Article  PubMed  Google Scholar 

Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci. 2017;405:81–90. https://doi.org/10.1016/j.ins.2017.04.012.

Article  Google Scholar 

Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11:1760. https://doi.org/10.1038/s41467-020-15432-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mousavi S, Afghah F, Acharya UR. HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks. Comput Biol Med. 2020;127:104057. https://doi.org/10.1016/j.compbiomed.2020.104057.

Article  PubMed  PubMed Central  Google Scholar 

Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, vanMaanen DP, Hartzel DN, Ruhl JA, Lagerman BF, et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation. 2021;143:1287–98. https://doi.org/10.1161/CIRCULATIONAHA.120.047829.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, 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;394:861–7. https://doi.org/10.1016/S0140-6736(19)31721-0.

Article  PubMed  Google Scholar 

Using Apple Watch for Arrhythmia Detection. https://www.apple.com/healthcare/docs/site/Apple_Watch_Arrhythmia_Detection.pdf. Accessed Feb. 20.

Fairbank T, DeBauge A, Harvey CJ, Jiwani S, Ranka S, Beaver TA, Sheldon SH, Reddy M, Noheria A. Electrocardiographic Z-axis QRS-T voltage-time-integral in patients with typical right bundle branch block - Correlation with echocardiographic right ventricular size and function. J Electrocardiol. 2024;82:73–9. https://doi.org/10.1016/j.jelectrocard.2023.11.004.

Article  PubMed  Google Scholar 

DeBauge A, Fairbank T, Harvey CJ, Ranka S, Jiwani S, Sheldon SH, Reddy M, Beaver TA, Noheria A. Electrocardiographic prediction of left ventricular hypertrophy in women and men with left bundle branch block - Comparison of QRS duration, amplitude and voltage-time-integral. J Electrocardiol. 2023;80:34–9. https://doi.org/10.1016/j.jelectrocard.2023.03.004.

Article  PubMed  PubMed Central  Google Scholar 

Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, Hahn RT, Tison G, Abreau SA, Barrios J, et al. Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease. J Am Coll Cardiol. 2022;80:613–26. https://doi.org/10.1016/j.jacc.2022.05.029.

Article  PubMed  Google Scholar 

Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, Murphree DH, Michelena HI, Enriquez-Sarano M, Carter RE, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021;42:2885–96. https://doi.org/10.1093/eurheartj/ehab153.

Article  PubMed  Google Scholar 

Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020;9:e014717. https://doi.org/10.1161/JAHA.119.014717.

Article  PubMed  PubMed Central  Google Scholar 

Huang PS, Tseng YH, Tsai CF, Chen JJ, Yang SC, Chiu FC, Chen ZW, Hwang JJ, Chuang EY, Wang YC, Tsai CT. An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease. Biomedicines. 2022;10. https://doi.org/10.3390/biomedicines10020394.

Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, Demuth SJ, Ackerman MJ, Gersh BJ, Arruda-Olson AM, et al. Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram. J Am Coll Cardiol. 2020;75:722–33. https://doi.org/10.1016/j.jacc.2019.12.030.

Article  PubMed  Google Scholar 

Siontis KC, Liu K, Bos JM, Attia ZI, Cohen-Shelly M, Arruda-Olson AM, Zanjirani Farahani N, Friedman PA, Noseworthy PA, Ackerman MJ. Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Int J Cardiol. 2021;340:42–7. https://doi.org/10.1016/j.ijcard.2021.08.026.

Article  PubMed  Google Scholar 

Sangha V, Dhingra LS, Oikonomou E, Aminorroaya A, Sikand NV, Sen S, Krumholz HM, Khera R. Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning. medRxiv. 2023. https://doi.org/10.1101/2023.12.23.23300490.

Lee Y, Choi B, Lee MS, Jin U, Yoon S, Jo YY, Kwon JM. An artificial intelligence electrocardiogram analysis for detecting cardiomyopathy in the peripartum period. Int J Cardiol. 2022;352:72–7. https://doi.org/10.1016/j.ijcard.2022.01.064.

Article  PubMed  Google Scholar 

Muchtar E, Gertz MA, Kumar SK, Lacy MQ, Dingli D, Buadi FK, Grogan M, Hayman SR, Kapoor P, Leung N, et al. Improved outcomes for newly diagnosed AL amyloidosis between 2000 and 2014: cracking the glass ceiling of early death. Blood. 2017;129:2111–9. https://doi.org/10.1182/blood-2016-11-751628.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Merlini G. A Step Forward in Solving Amyloidosis. N Engl J Med. 2023;389:1615–7. https://doi.org/10.1056/NEJMe2309308.

Article  PubMed  Google Scholar 

Grogan M, Lopez-Jimenez F, Cohen-Shelly M, Dispenzieri A, Attia ZI, Abou Ezzedine OF, Lin G, Kapa S, Borgeson DD, Friedman PA, Murphree DH Jr. Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis. Mayo Clin Proc. 2021;96:2768–78. https://doi.org/10.1016/j.mayocp.2021.04.023.

Article  PubMed  Google Scholar 

•• Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25:70–4. https://doi.org/10.1038/s41591-018-0240-2. This paper from the Mayo Clinic AI-ECG group launched the race on structural heart disease diagnostics with prediction of left ventricular systolic dysfucntion from ECG signal data alone. In addition to demonstrating the capability of AI-ECG in identifying reduced EF, this short letter/focus paper is also a model in terms of AI-ECG research best-practices.

Article  CAS  PubMed  Google Scholar 

Attia ZI, Kapa S, Yao X, Lopez-Jimenez F, Mohan TL, Pellikka PA, Carter RE, Shah ND, Friedman PA, Noseworthy PA. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol. 2019;30:668–74. https://doi.org/10.1111/jce.13889.

Article  PubMed  Google Scholar 

Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, et al. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol. 2020;13:e008437. https://doi.org/10.1161/CIRCEP.120.008437.

Article  CAS  PubMed  Google Scholar 

Mahayni AA, Attia ZI, Medina-Inojosa JR, Elsisy MFA, Noseworthy PA, Lopez-Jimenez F, Kapa S, Asirvatham SJ, Friedman PA, Crestenallo JA, Alkhouli M. Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery. Mayo Clin Proc. 2021;96:3062–70. https://doi.org/10.1016/j.mayocp.2021.06.024.

Article  PubMed  Google Scholar 

Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, Friedman PA, Lopez-Jimenez F. Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circ Arrhythm Electrophysiol. 2020;13:e007988. https://doi.org/10.1161/CIRCEP.119.007988.

Article  PubMed  PubMed Central  Google Scholar 

Rushlow DR, Croghan IT, Inselman JW, Thacher TD, Friedman PA, Yao X, Pellikka PA, Lopez-Jimenez F, Bernard ME, Barry BA, et al. Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. Mayo Clin Proc. 2022;97:2076–85. https://doi.org/10.1016/j.mayocp.2022.04.008.

Article  PubMed  Google Scholar 

Lou YS, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease. J Pers Med. 2022;12. https://doi.org/10.3390/jpm12020315.

Tison GH, Zhang J, Delling FN, Deo RC. Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery. Circ Cardiovasc Qual Outcomes. 2019;12:e005289. https://doi.org/10.1161/CIRCOUTCOMES.118.005289.

Article  PubMed