Enriquez-Sarano M, Tajik AJ, Schaff HV, Orszulak TA, Bailey KR, Frye RL (1994) Echocardiographic prediction of survival after surgical correction of organic mitral regurgitation. Circulation 90(2):830–837

Article  CAS  PubMed  Google Scholar 

Schreiber TL, Fisher J, Mangla A, Miller D (1989) Severe “silent” mitral regurgitation. A potentially reversible cause of refractory heart failure. Chest 96(2):242–246

Article  CAS  PubMed  Google Scholar 

Schaff HV (2009) Asymptomatic severe mitral valve regurgitation: observation or operation? Circulation 119(6):768–769

Article  PubMed  Google Scholar 

Maganti K, Rigolin VH, Sarano ME, Bonow RO (2010) Valvular heart disease: diagnosis and management. Mayo Clin Proc 85(5):483–500

Article  PubMed  PubMed Central  Google Scholar 

Kwon JM, Kim KH, Akkus Z, Jeon KH, Park J, Oh BH (2020) Artificial intelligence for detecting mitral regurgitation using electrocardiography. J Electrocardiol 59:151–157

Article  PubMed  Google Scholar 

Hirota N, Suzuki S, Arita T, Yagi N, Otsuka T, Yamashita T (2021) Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease. BMC Geriatr 21(1):460

Article  PubMed  PubMed Central  Google Scholar 

Suzuki S, Yamashita T, Otsuka T, Sagara K, Uejima T, Oikawa Y, Yajima J, Koike A, Nagashima K, Kirigaya H, Ogasawara K, Sawada H, Aizawa T (2011) Recent mortality of Japanese patients with atrial fibrillation in an urban city of Tokyo. J Cardiol 58(2):116–123

Article  PubMed  Google Scholar 

Zoghbi WA, Adams D, Bonow RO, Enriquez-Sarano M, Foster E, Grayburn PA, Hahn RT, Han Y, Hung J, Lang RM, Little SH, Shah DJ, Shernan S, Thavendiranathan P, Thomas JD, Weissman NJ (2017) Recommendations for noninvasive evaluation of native valvular regurgitation: a report from the american society of echocardiography developed in collaboration with the society for cardiovascular magnetic resonance. J Am Soc Echocardiogr 30(4):303–371

Article  PubMed  Google Scholar 

Suzuki S, Yamashita T, Otsuka T, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Matsuhama M, Iida M, Inoue T, Yajima J (2021) Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: a retrospective approach based on the risk probability for clinical events. Int J Cardiol Heart Vasc 37:100883

PubMed  PubMed Central  Google Scholar 

Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A (2021) Machine learning for brain age prediction: introduction to methods and clinical applications. EBioMedicine 72:103600

Article  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, Kapa S, Friedman PA (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394(10201):861–867

Article  PubMed  Google Scholar 

Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hirota N, Hyodo A, Satoh K, Otsuka T, Arita T, Yagi N, Yamashita T (2022) Identifying patients with atrial fibrillation during sinus rhythm on ECG: significance of the labeling in the artificial intelligence algorithm. Int J Cardiol Heart Vasc 38:100954

PubMed  PubMed Central  Google Scholar 

Hirota N, Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hyodo A, Satoh K, Arita T, Yagi N, Otsuka T, Yamashita T (2023) Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms. Int J Cardiol Heart Vasc 44:101172

PubMed  PubMed Central  Google Scholar 

Nagi J, Ducatelle F, Di Caro A, Ciresan D, Meier U, Giusti A, Nagi F, Schmidhuber J, Gambardella M (2011) Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: IEEE international conference on signal and image processing applications, Kuala Lumpur, pp 342–347

He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv pre-print server

Coffin M, Sukhatme S (1997) Receiver operating characteristic studies and measurement errors. Biometrics 53(3):823–837

Article  CAS  PubMed  Google Scholar 

Gruwez H, Barthels M, Haemers P, Verbrugge FH, Dhont S, Meekers E, Wouters F, Nuyens D, Pison L, Vandervoort P, Pierlet N (2023) Detecting paroxysmal atrial fibrillation from an electrocardiogram in sinus rhythm: external validation of the AI approach. JACC Clin Electrophysiol 9:1771–1782

Article  PubMed  Google Scholar 

Attia IZ, Tseng AS, Benavente ED, Medina-Inojosa JR, Clark TG, Malyutina S, Kapa S, Schirmer H, Kudryavtsev AV, Noseworthy PA, Carter RE, Ryabikov A, Perel P, Friedman PA, Leon DA, Lopez-Jimenez F (2021) External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Int J Cardiol 329:130–135

Article  PubMed  PubMed Central  Google Scholar 

Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA (2020) Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea. Circ Arrhythm Electrophysiol 13(8):e008437

Article  CAS  PubMed  Google Scholar 

Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satem G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA (2019) Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 25(1):70–74

Article  CAS  PubMed  Google Scholar 

Hirota N, Suzuki S, Motogi J, Umemoto T, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Otsuka T, Uejima T, Oikawa Y, Hori T, Matsuhama M, Iida M, Yajima J, Yamashita T (2024) Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting. Heart Vessels 39(6):524–538

Article  PubMed  Google Scholar 

Maanja M, Noseworthy PA, Geske JB, Ackerman MJ, Arruda-Olson AM, Ommen SR, Attia ZI, Friedman PA, Siontis KC (2022) Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice. Cardiovasc Digit Health J 3(6):289–296

Article  PubMed  PubMed Central  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 (2021) Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Int J Cardiol 340:42–47

Article  PubMed  Google Scholar 

Arita T, Suzuki S, Motogi J, Umemoto T, Hirota N, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Yagi N, Otsuka T, Kishi M, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Hori T, Matsuhama M, Iida M, Yajima J, Yamashita T (2024) Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network. Int J Cardiol Heart Vasc 51:101389

PubMed  PubMed Central  Google Scholar