Application of mobile health, telemedicine and artificial intelligence to echocardiography

2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.005 · Cited by 4,945 · see citing works

Current and Evolving Echocardiographic Techniques for the Quantitative Evaluation of Cardiac Mechanics: ASE/EAE Consensus Statement on Methodology and Indications

Mor‐Avi, V., Lang, R. M., Badano, L. P., Bëlohlávek, M., Cardim, N. M., Dérumeaux, G., Galderisi, M., Marwick, T. H., Nagueh, S. F., Sengupta, P. P., et al. (2011) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2011.01.015 · Cited by 1,258 · see citing works

Artificial intelligence in cardiology

Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works

Artificial Intelligence in Precision Cardiovascular Medicine

Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126 · see citing works

Fully Automated Echocardiogram Interpretation in Clinical Practice: Feasibility and Diagnostic Accuracy

Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006 · see citing works

Telehealth

Tuckson, R. V., Edmunds, M., & Hodgkins, M. L. (2017) · New England Journal of Medicine · doi:10.1056/nejmsr1503323 · Cited by 907 · see citing works

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-018-0471-x · Cited by 773 · see citing works

European Heart Journal(1984)

(1984) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a061650 · Cited by 664 · see citing works

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards

Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638 · see citing works

Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch

Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.0136 · Cited by 566 · see citing works

Machine learning in cardiovascular medicine: are we there yet?

Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555 · see citing works

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., van Rosendael, A. R., Beecy, A. N., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehy404 · Cited by 534 · see citing works

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association

Burke, L. E., Ma, J., Azar, K. M. J., Bennett, G. G., Peterson, E. D., Zheng, Y., Riley, W., Stephens, J., Shah, S. H., Suffoletto, B., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000232 · Cited by 516 · see citing works

Mobile technology and the digitization of healthcare

Bhavnani, S. P., Narula, J., & Sengupta, P. P. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv770 · Cited by 460 · see citing works

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

Narula, S., Shameer, K., Omar, A. M. S., Dudley, J. T., & Sengupta, P. P. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.08.062 · Cited by 414 · see citing works

JACC: Cardiovascular Imaging 2.0

Chandrashekhar, Y., & Fuster, V. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.03.005 · Cited by 242 · see citing works

Pocket-size hand-held cardiac ultrasound as an adjunct to clinical examination in the hands of medical students and junior doctors

Panoulas, V. F., Daigeler, A.-L., Malaweera, A. S. N., Lota, A. S., Baskaran, D., Rahman, S., & Nihoyannopoulos, P. (2012) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jes140 · Cited by 241 · see citing works

Variability in Ejection Fraction Measured By Echocardiography, Gated Single-Photon Emission Computed Tomography, and Cardiac Magnetic Resonance in Patients With Coronary Artery Disease and Left Ventricular Dysfunction

Pellikka, P. A., She, L., Holly, T. A., Lin, G., Varadarajan, P., Pai, R. G., Bonow, R. O., Pohost, G. M., Panza, J. A., Berman, D. S., et al. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.1456 · Cited by 238 · see citing works

Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm

Tsang, W., Salgo, I. S., Medvedofsky, D. A., Takeuchi, M., Prater, D. M., Weinert, L., Yamat, M., Mor‐Avi, V., Patel, A. R., & Lang, R. M. (2016) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2015.12.020 · Cited by 223 · see citing works

Cognitive Machine-Learning Algorithm for Cardiac Imaging

Sengupta, P. P., Huang, Y., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., Gall, W., & Dudley, J. T. (2016) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.115.004330 · Cited by 205 · see citing works

Diagnostic Accuracy of a Hand-Held Ultrasound Scanner in Routine Patients Referred for Echocardiography

Prinz, C., & Voigt, J. (2010) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2010.10.017 · Cited by 200 · see citing works

mHealth in Cardiovascular Health Care

Chow, C. K., Ariyarathna, N., Islam, S. M. S., Thiagalingam, A., & Redfern, J. (2016) · Heart Lung and Circulation · doi:10.1016/j.hlc.2016.04.009 · Cited by 197 · see citing works

Handheld Echocardiography

Chamsi‐Pasha, M. A., Sengupta, P. P., & Zoghbi, W. A. (2017) · Circulation · doi:10.1161/circulationaha.117.026622 · Cited by 170 · see citing works

Circulation: Cardiovascular Imaging

(2016) · Circulation Cardiovascular Imaging · doi:10.1161/hci.0000000000000011 · Cited by 156 · see citing works

Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease

Cook, N. R., & Ridker, P. M. (2016) · Annals of Internal Medicine · doi:10.7326/m16-1739 · Cited by 155 · see citing works

Feasibility and reliability of point-of-care pocket-sized echocardiography

Andersen, G. N., Haugen, B. O., Graven, T., Salvesen, Ø. O., Mjølstad, O. C., & Dalen, H. (2011) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ejechocard/jer108 · Cited by 146 · see citing works

Improved cardiovascular diagnostic accuracy by pocket size imaging device in non-cardiologic outpatients: the NaUSiCa (Naples Ultrasound Stethoscope in Cardiology) study

Galderisi, M., Santoro, A., Versiero, M., Lomoriello, V. S., Esposito, R., Raia, R., Farina, F., Schiattarella, P. L., Bonito, M., Olibet, M., et al. (2010) · Cardiovascular Ultrasound · doi:10.1186/1476-7120-8-51 · Cited by 139 · see citing works

Using a novel wireless system for monitoring patients after the atrial fibrillation ablation procedure: The iTransmit study

Tarakji, K. G., Wazni, O. M., Callahan, T. D., Kanj, M. H., Hakim, A. H., Wolski, K., Wilkoff, B. L., Saliba, W. I., & Lindsay, B. D. (2014) · Heart Rhythm · doi:10.1016/j.hrthm.2014.11.015 · Cited by 138 · see citing works

Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

Abdolmanafi, A., Duong, L., Dahdah, N., & Farida Cheriet, A. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.001203 · Cited by 130 · see citing works

Cardiac Point-of-Care Ultrasound: State-of-the-Art in Medical School Education

Johri, A. M., Durbin, J., Newbigging, J., Tanzola, R. C., Chow, R. D., De, S. K., & Tam, J. W. (2018) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2018.01.014 · Cited by 125 · see citing works

Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study

Medvedofsky, D. A., Mor‐Avi, V., Amzulescu, M. S., Fernández‐Golfín, C., Hinojar, R., Monaghan, M. J., Otani, K., Reiken, J., Takeuchi, M., Tsang, W., et al. (2016) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jew328 · Cited by 125 · see citing works

Routinely adding ultrasound examinations by pocket-sized ultrasound devices improves inpatient diagnostics in a medical department

Mjølstad, O. C., Dalen, H., Graven, T., Kleinau, J. O., Salvesen, Ø. O., & Haugen, B. O. (2011) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2011.10.009 · Cited by 123 · see citing works

Diagnosing Acute Heart Failure in Patients With Undifferentiated Dyspnea: A Lung and Cardiac Ultrasound (Lu CUS ) Protocol

Russell, F. M., Ehrman, R. R., Cosby, K. S., Ansari, A., Tseeng, S., Christain, E., & Bailitz, J. M. (2015) · Academic Emergency Medicine · doi:10.1111/acem.12570 · Cited by 121 · see citing works

American Society of Echocardiography: Remote Echocardiography with Web-Based Assessments for Referrals at a Distance (ASE-REWARD) Study

Singh, S., Bansal, M., Maheshwari, P., Adams, D. B., Sengupta, S. P., Price, R., Dantin, L., Smith, M., Kasliwal, R. R., Pellikka, P. A., et al. (2013) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2012.12.012 · Cited by 109 · see citing works

Accuracy and Usability of a Self-Administered 6-Minute Walk Test Smartphone Application

Brooks, G. C., Vittinghoff, E., Iyer, S., Tandon, D., Kuhar, P., Madsen, K. A., Marcus, G. M., Pletcher, M. J., & Olgin, J. E. (2015) · Circulation Heart Failure · doi:10.1161/circheartfailure.115.002062 · Cited by 108 · see citing works

Phenotypic Clustering of Left Ventricular Diastolic Function Parameters

Lancaster, M. C., Omar, A. M. S., Narula, S., Kulkarni, H. R., Narula, J., & Sengupta, P. P. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.02.005 · Cited by 102 · see citing works

Primary Results of the HABIT Trial (Heart Failure Assessment With BNP in the Home)

Maisel, A. S., Barnard, D. D., Jaski, B. E., Frivold, G., Marais, J., Azer, M., Miyamoto, M. I., Lombardo, D. M., Kelsay, D., Borden, K., et al. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.01.052 · Cited by 98 · see citing works

Evaluation of a new pocket echoscopic device for focused cardiac ultrasonography in an emergency setting

Biais, M., Carrié, C., Delaunay, F., Morel, N., Revel, P., & Janvier, G. (2012) · Critical Care · doi:10.1186/cc11340 · Cited by 94 · see citing works

Imaging Congestion With a Pocket Ultrasound Device: Prognostic Implications in Patients With Chronic Heart Failure

Gustafsson, M., Alehagen, U., & Johansson, P. (2015) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2015.02.004 · Cited by 90 · see citing works

Diagnostic accuracy of pocket-size handheld echocardiographs used by cardiologists in the acute care setting

Testuz, A., Muller, H., Keller, P., Meyer, P., Stampfli, T., Šekoranja, L., Vuille, C., & Burri, H. (2012) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jes085 · Cited by 83 · see citing works

A randomized trial of pocket-echocardiography integrated mobile health device assessments in modern structural heart disease clinics

Bhavnani, S. P., Sola, S., Adams, D., Venkateshvaran, A., Dash, P. K., Sengupta, P. P., Bhavnani, S., Sola, S., Venkateshvaran, A., Adams, D., et al. (2018) · JACC: Cardiovascular Imaging · doi:10.1016/j.jcmg.2017.06.019 · Cited by 81 · see citing works

Interpretation of Remotely Downloaded Pocket-Size Cardiac Ultrasound Images on a Web-Enabled Smartphone: Validation Against Workstation Evaluation

Choi, B. G., Mukherjee, M., Dala, P., Young, H. A., Tracy, C. M., Katz, R. J., & Lewis, J. F. (2011) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2011.08.007 · Cited by 81 · see citing works

Robot-Assisted Remote Echocardiographic Examination and Teleconsultation

Boman, K., Olofsson, M., Berggren, P., Sengupta, P. P., & Narula, J. (2014) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2014.05.006 · Cited by 78 · see citing works

Pocket-Sized Transthoracic Echocardiography Device for the Measurement of Cardiac Chamber Size and Function

Fukuda, S., Shimada, K., Kawasaki, T., Fujimoto, H., Maeda, K., Inanami, H., Yoshida, K., Jissho, S., Taguchi, H., Yoshiyama, M., et al. (2009) · Circulation Journal · doi:10.1253/circj.cj-08-1076 · Cited by 74 · see citing works

Three-Dimensional Echocardiographic Automated Quantification of Left Heart Chamber Volumes Using an Adaptive Analytics Algorithm: Feasibility and Impact of Image Quality in Nonselected Patients

Medvedofsky, D. A., Mor‐Avi, V., Byku, I., Singh, A., Weinert, L., Yamat, M., Kruse, E., Ciszek, B., Nelson, A., Otani, K., et al. (2017) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2017.05.018 · Cited by 64 · see citing works

Novel Description of the 24-Hour Circadian Rhythms of Brachial Versus Central Aortic Blood Pressure and the Impact of Blood Pressure Treatment in a Randomized Controlled Clinical Trial

Williams, B., Lacy, P. S., Baschiera, F., Brunel, P., & Düsing, R. (2013) · Hypertension · doi:10.1161/hypertensionaha.111.00763 · Cited by 61 · see citing works

A Novel and Simple Method Using Pocket-Sized Echocardiography to Screen for Aortic Stenosis

Abe, Y., Ito, M., Tanaka, C., Ito, K., Naruko, T., Itoh, A., Haze, K., Muro, T., Yoshiyama, M., & Yoshikawa, J. (2013) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2013.03.008 · Cited by 51 · see citing works

Defining a Mobile Health Roadmap for Cardiovascular Health and Disease

Eapen, Z. J., Turakhia, M. P., McConnell, M. V., Graham, G. N., Dunn, P. J., Tiner, C., Rich, C., Harrington, R. A., Peterson, E. D., & Wayte, P. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.003119 · Cited by 51 · see citing works

The Academic Medical System

Konstam, M. A., Hill, J. A., Kovacs, R. J., Harrington, R. A., Arrighi, J. A., & Khera, A. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.12.024 · Cited by 46 · see citing works

Diagnostic influence of cardiovascular screening by pocket-size ultrasound in a cardiac unit

Skjetne, K., Graven, T., Haugen, B. O., Salvesen, Ø. O., Kleinau, J. O., & Dalen, H. (2011) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ejechocard/jer111 · Cited by 44 · see citing works

Value of Interactive Scanning for Improving the Outcome of New-Learners in Transcontinental Tele-Echocardiography (VISION-in-Tele-Echo) Study

Bansal, M., Singh, S., Maheshwari, P., Adams, D. B., McCulloch, M. L., Dada, T., Sengupta, S. P., Kasliwal, R. R., Pellikka, P. A., & Sengupta, P. P. (2014) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.09.001 · Cited by 43 · see citing works

Efficacy and Accuracy of Novel Automated Mitral Valve Quantification: Three‐Dimensional Transesophageal Echocardiographic Study

Kagiyama, N., Toki, M., Hara, M., Fukuda, S., Aritaka, S., Miki, T., Ohara, M., Hayashida, A., Hirohata, A., Yamamoto, K., et al. (2015) · Echocardiography · doi:10.1111/echo.13135 · Cited by 38 · see citing works

Circulation: Heart Failure

(2016) · Circulation Heart Failure · doi:10.1161/hhf.0000000000000010 · Cited by 38 · see citing works

Diagnostic Utility and Clinical Usefulness of the Pocket Echocardiographic Device

Michalski, B. W., Kasprzak, J. D., Szymczyk, E., & Lipiec, P. (2011) · Echocardiography · doi:10.1111/j.1540-8175.2011.01553.x · Cited by 36 · see citing works

Diagnostic Performance of Handheld Echocardiography for the Assessment of Basic Cardiac Morphology and Function: A Validation Study in Routine Cardiac Patients

Prinz, C., Dohrmann, J., van Buuren, F., Bitter, T., Bogunović, N., Horstkotte, D., & Faber, L. (2012) · Echocardiography · doi:10.1111/j.1540-8175.2012.01728.x · Cited by 35 · see citing works

Validation of the Smallest Pocket Echoscopic Device’s Diagnostic Capabilities in Heart Investigation

Lafitte, S., Alimazighi, N., Réant, P., Dijos, M., Zaroui, A., Mignot, A., Lafitte, M., Pillois, X., Roudaut, R. P., & DeMaria, A. N. (2011) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2011.02.010 · Cited by 34 · see citing works

Can Hospital Rounds With Pocket Ultrasound By Cardiologists Reduce Standard Echocardiography?

Khan, H. A., Wineinger, N. E., Uddin, P. Q., Mehta, H. S., Rubenson, D. S., & Topol, E. J. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.03.015 · Cited by 34 · see citing works

Buckling of the transoesophageal echocardiography probe: an unusual complication during conscious sedation

Andersen, M. J., Hassager, C., Steinbrüchel, D. A., & Møller, J. E. (2011) · European Journal of Echocardiography · doi:10.1093/ejechocard/jer035 · Cited by 32 · see citing works

Assessment of left atrial size in addition to focused cardiopulmonary ultrasound improves diagnostic accuracy of acute heart failure in the Emergency Department

Carlino, M. V., Paladino, F., Sforza, A., Serra, C., Liccardi, F., SIMONE, G. D., & Mancusi, C. (2018) · Echocardiography · doi:10.1111/echo.13851 · Cited by 29 · see citing works

Identification of the main determinants of abdominal aorta size: a screening by Pocket Size Imaging Device

Esposito, R., Ilardi, F., Lomoriello, V. S., Sorrentino, R., Sellitto, V., Giugliano, G., Esposito, G., Trimarco, B., & Galderisi, M. (2017) · Cardiovascular Ultrasound · doi:10.1186/s12947-016-0094-z · Cited by 23 · see citing works

Point-of-care echocardiography for aortic dissection, pulmonary embolism and acute coronary syndrome in patients with killer chest pain: EASY screening focused on the assessment of effusion, aorta, ventricular size and shape and ventricular asynergy

Nishigami, K. (2015) · Journal of Echocardiography · doi:10.1007/s12574-015-0265-1 · Cited by 20 · see citing works

Hand-held echocardiography in the setting of pre-operative cardiac evaluation of patients undergoing non-cardiac surgery: results from a randomized pilot study

Cavallari, I., Mega, S., Goffredo, C., Patti, G., Chello, M., & Sciascio, G. D. (2015) · International journal of cardiac imaging · doi:10.1007/s10554-015-0656-4 · Cited by 11 · see citing works

Bedside screening with the use of pocket‐size imaging device can be useful for ruling out carotid artery stenosis in patients scheduled for cardiac surgery

Filipiak‐Strzecka, D., Kasprzak, J. D., Szymczyk, E., Wejner‐Mik, P., & Lipiec, P. (2017) · Echocardiography · doi:10.1111/echo.13507 · Cited by 10 · see citing works

Prediction of aortic stenosis-related events in patients with systolic ejection murmur using pocket-sized echocardiography

Furukawa, A., Abe, Y., Ito, M., Tanaka, C., Ito, K., Komatsu, R., Haze, K., Naruko, T., Yoshiyama, M., & Yoshikawa, J. (2016) · Journal of Cardiology · doi:10.1016/j.jjcc.2016.02.021 · Cited by 7 · see citing works

Advantages and pitfalls of pocket ultrasound vs daily chest radiography in the coronary care unit: A single‐user experience

Phillips, C. T., & Manning, W. J. (2017) · Echocardiography · doi:10.1111/echo.13509 · Cited by 6 · see citing works

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

Déniz-García, A., Fabelo, H. A., Rodríguez-Almeida, A. J., Zamora-Zamorano, G., Castro-Fernández, M., Alberiche-Ruano, M. P., Solvoll, T. G., Granja, C., Schopf, T. R., Callicó, G. M., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/44030 · Cited by 293 · see citing works

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

Aminizadeh, S., Heidari, A., Dehghan, M., Toumaj, S., Rezaei, M., Navimipour, N. J., Stroppa, F., & Ünal, M. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102779 · Cited by 254 · see citing works

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works

Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology

Marey, A., Arjmand, P., alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-024-01356-2 · Cited by 223 · see citing works

Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease

Kagiyama, N., Shrestha, S., Farjo, P. D., & Sengupta, P. P. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012788 · Cited by 183 · see citing works

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

Zhou, J., Du, M., Chang, S., & Chen, Z. (2021) · Cardiovascular Ultrasound · doi:10.1186/s12947-021-00261-2 · Cited by 155 · see citing works

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Romiti, S., Vinciguerra, M., Saade, W., Cortajarena, I. A., & Greco, E. (2020) · Cardiology Research and Practice · doi:10.1155/2020/4972346 · Cited by 107 · see citing works

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser, M. A., Majeed, A. A., Alsabah, M. Q., Al-Shaikhli, T. R., & Kaky, K. M. (2024) · Algorithms · doi:10.3390/a17020078 · Cited by 124 · see citing works

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74 · see citing works

Deep learning for dermatologists: Part II. Current applications

Puri, P., Comfere, N. I., Drage, L. A., Shamim, H., Bezalel, S. A., Pittelkow, M. R., Davis, M. D. P., Wang, M., Mangold, A. R., Tollefson, M. M., et al. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.05.053 · Cited by 67 · see citing works

Potential for Use of Portable Ultrasound Devices in Rural and Remote Settings in Australia and Other Developed Countries: A Systematic Review

Shaddock, L., & Smith, T. (2022) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s359084 · Cited by 64 · see citing works

Machine learning in TCM with natural products and molecules: current status and future perspectives

Ma, S., Liu, J., Li, W., Liu, Y., Hui, X., Qu, P., Jiang, Z., Li, J., & Wang, J. (2023) · Chinese Medicine · doi:10.1186/s13020-023-00741-9 · Cited by 59 · see citing works

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

Seetharam, K., Brito, D., Farjo, P. D., & Sengupta, P. P. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.618849 · Cited by 61 · see citing works

Acceptance of the Use of Artificial Intelligence in Medicine Among Japan’s Doctors and the Public: A Questionnaire Survey

Tamori, H., Yamashina, H., Mukai, M., Morii, Y., Suzuki, T., & Ogasawara, K. (2021) · JMIR Human Factors · doi:10.2196/24680 · Cited by 52 · see citing works

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Seetharam, K., Raina, S., & Sengupta, P. P. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01329-7 · Cited by 52 · see citing works

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

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Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021) · Sustainable Operations and Computers · doi:10.1016/j.susoc.2021.04.003 · Cited by 47 · see citing works

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Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling

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2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

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Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review

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Applications of Machine Learning in Cardiology

Seetharam, K., Balla, S., Bianco, C. M., Cheung, J. W., PACHULSKI, R. T., Asti, D., Nalluri, N., Tejpal, A., Mir, P., Shah, J. M., et al. (2022) · Cardiology and Therapy · doi:10.1007/s40119-022-00273-7 · Cited by 44 · see citing works

Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans

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Artificial Intelligence and Machine Learning in Cardiovascular Imaging

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Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Seetharam, K., Shrestha, S., & Sengupta, P. P. (2021) · Interventional Cardiology Reviews Research Resources · doi:10.15420/icr.2020.04 · Cited by 33 · see citing works

How Social Influence Promotes the Adoption of Mobile Health among Young Adults in China: A Systematic Analysis of Trust, Health Consciousness, and User Experience

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A new era in colorectal cancer: Artificial Intelligence at the forefront

Farhoudian, A., Heidari, A., & Shahhosseini, R. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110926 · Cited by 26 · see citing works

Pre-clinical medical student cardiac point-of-care ultrasound curriculum based on the American Society of Echocardiography recommendations: a pilot and feasibility study

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Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

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Artificial Intelligence in Cardiac Imaging

Seetharam, K., Shrestha, S., & Sengupta, P. P. (2020) · US Cardiology Review · doi:10.15420/usc.2019.19.2 · Cited by 19 · see citing works

Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review

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Cardiac Diagnosis with Machine Learning: A Paradigm Shift in Cardiac Care

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2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

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A Complete Study on Machine Learning Algorithms for Medical Data Analysis

Kaur, I., & Ali, A. A. (2024) · Wiley eBooks · doi:10.1002/9781394175345.ch7 · Cited by 17 · see citing works

Artificial Intelligence Applied to Telemedicine: opportunities for healthcare delivery in rural areas

Cano, A. M. C. (2023) · LatIA · doi:10.62486/latia20233 · Cited by 14 · see citing works

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab001 · Cited by 16 · see citing works

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Seetharam, K., Sengupta, P. P., & Bianco, C. M. (2020) · Echocardiography · doi:10.1111/echo.14764 · Cited by 13 · see citing works

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Precision Medicine for Cancer and Health Equity in Latin America: Generating Understanding for Policy and Health System Shaping

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Seetharam, K., Thyagaturu, H. S., Ferreira, G. L., Patel, A., Patel, C. P., Elahi, A., PACHULSKI, R. T., Shah, J. M., Mir, P., Thodimela, A., et al. (2024) · Cardiology and Therapy · doi:10.1007/s40119-024-00368-3 · Cited by 8 · see citing works

'The AI is uncertain, so am I. What now?': Navigating Shortcomings of Uncertainty Representations in Human-AI Collaboration with Capability-focused Guidance

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Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation

Wan, X., Ge, L., Tian, J., Ding, G., Gao, B., Geng, H., Ge, C., & Song, X. (2026) · Clinical Medicine Insights Cardiology · doi:10.1177/11795468261421344 · Cited by 8 · see citing works

Research Challenges and Opportunities Towards a Holistic View of Telemedicine Systems: A Systematic Review

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Telemedicine in Healthcare System: A Discussion Regarding Several Practices

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Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review

Restrepo-Parra, E., Ariza-Colpas, P., Torres-Bonilla, L. V., Piñeres-Melo, M. A., Urina‐Triana, M., & Butt, S. A. (2024) · IoT · doi:10.3390/iot5030024 · Cited by 6 · see citing works

From Index Medicus to the Palm of Our Hands—What’s “App-ening” in Graduate Medical Education

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Mobile Apps for Helping Patient-Users: Is It Still Far-Fetched?

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2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals

Varma, N., Cygankiewicz, I., & Turakhia, M. P. (2021) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2021-4420 · Cited by 6 · see citing works

Application of perioperative hemodynamics today and potentials for tomorrow

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Artificial intelligence and machine learning in cardiovascular computed tomography

Seetharam, K., Bhat, P., Orris, M., Prabhu, H., Shah, J. M., Asti, D., Chawla, P., & Mir, T. (2021) · World Journal of Cardiology · doi:10.4330/wjc.v13.i10.546 · Cited by 5 · see citing works

Role of Deep Learning in Computed Tomography

Garg, Y., Seetharam, K., Sharma, M., Rohita, D., & Nabi, W. (2023) · Cureus · doi:10.7759/cureus.39160 · Cited by 4 · see citing works

Classification of Artificial Intelligence Based Coronary Artery Stenosis

Ece, Y., Çolak, T., Uzun, S., & Sağiroğlu, A. O. (2022) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs22161548 · Cited by 3 · see citing works

Artificial intelligence in cardiovascular imaging

Seetharam, K., & Min, J. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00019-3 · Cited by 3 · see citing works

Pros and Cons of Telemedicine: Implications in Cardiology and Cardiovascular Medicine

Gerges, S., & Hallit, S. R. (2022) · Future Cardiology · doi:10.2217/fca-2022-0065 · Cited by 3 · see citing works

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus

Durr, A. J., Korol, A. S., Hathaway, Q. A., Kunovac, A., Taylor, A. D., Rizwan, S., Pinti, M. V., & Hollander, J. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285512 · Cited by 3 · see citing works

Investigating the Ethical Implications of Artificial Intelligence and Establishing Guidelines for Responsible AI Development

Sukumar, R., Gambhir, V., & Seth, J. (2024) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset62108.2024.10743915 · Cited by 3 · see citing works

Understanding Plant Secondary Metabolism Using Bioinformatics Tools

Mukherjee, D., & Mukherjee, A. (2024) · Wiley eBooks · doi:10.1002/9781394209965.ch19 · Cited by 3 · see citing works

Technological advances within digital medicine

Altibi, A. M. A., Alaref, S. J., & Khosravi, P. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00001-4 · Cited by 2 · see citing works

Reasons for disagreement between screening and standard echocardiography in primary care: data from the PROVAR + study

Galdino, B. F., Amaral, A. M., dos Santos, L. P. A., Nogueira, M. A. A., Rocha, R. T. L., Nunes, M. C. P., Beaton, A., Oliveira, K., Franco, J., Barbosa, M. M., et al. (2023) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-023-02800-6 · Cited by 2 · see citing works

Development Status and Prospect of Remote Diagnosis and Treatment of Echocardiography Worldwide

Liu, M., Duan, M. S., Li, P. Y., Liu, M. R., Wu, M. Y., & Zhang, M. L. (2020) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2020.200047 · Cited by 2 · see citing works

AI In Telemedicine

Kilari, S. D. (2023) · International Journal of Scientific Research and Management (IJSRM) · doi:10.18535/ijsrm/v11i04.mp3 · Cited by 2 · see citing works

Organizational considerations from HFE to speed up the adoption of AI-related technology in medical diagnostics

Elkefi, S., Wang, H., & Asan, O. (2020) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857920091060 · Cited by 2 · see citing works

Amelioration of Mitochondrial Bioenergetic Dysfunction in Diabetes Mellitus: Delving into Specialized and Non-specific Therapeutics for the Ailing Heart

Durr, A. J. (2022) · doi:10.33915/etd.11325 · Cited by 2 · see citing works

Breakthrough in Management of Cardiovascular Diseases by Artificial Intelligence in Healthcare Settings

Gunturu, L. N., Dornadula, G., & Nimbagal, R. N. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815080445123020014 · Cited by 1 · see citing works

Exploring heart dissection techniques for enhancing anatomical education: a pilot study to replicate transthoracic echocardiography

Truong, H. C., Myagmardorj, Y., Miyaso, H., & Kawagishi, K. (2024) · Surgical and Radiologic Anatomy · doi:10.1007/s00276-024-03342-9 · Cited by 1 · see citing works

Diagnosis of non-puerperal mastitis based on “whole tongue” features: non-invasive biomarker mining and diagnostic model construction

Tu, S., Yin, Y., Ma, L., Chen, H., & Ye, M. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1602883 · Cited by 1 · see citing works

Tele-Echocardiography: An Effective Solution for Expanding Access to Cardiology Services in Low-Resource Settings: A Narrative Review

Jain, U. N., Shahu, S., Chandak, R. M., & Bhajbhuje, A. N. (2025) · Journal of The Indian Academy of Echocardiography & Cardiovascular Imaging · doi:10.4103/jiae.jiae_17_25 · Cited by 1 · see citing works

Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review

Papadopoulos, C. H., Karelas, D., Floropoulou, C., Tzavida, K., Oikonomidis, D. L., Tasoulis, A., Tatsis, E., Kouloulias, I., & Kadoglou, N. P. E. (2026) · BioTech · doi:10.3390/biotech15010022 · Cited by 1 · see citing works

Case of Supracristal Ventricular Septal Defect and Aortic Regurgitation Detected by Cardiac Auscultation but Missed by Diagnostic Imaging

Seetharam, K., & PACHULSKI, R. T. (2021) · Cureus · doi:10.7759/cureus.13502 · Cited by 1 · see citing works

Cardiac Ultrasound Imaging: The Role of Artificial Intelligence

Seetharam, K., & Sengupta, P. P. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_38 · Cited by 1 · see citing works

Artificial intelligence in coronary physiology: where do we stand?

Mody, R., Dash, D., & Mody, D. (2022) · Journal of Transcatheter Interventions · doi:10.31160/jotci202230a20220009 · Cited by 1 · see citing works

IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR CARDIAC ARREST PREDICTION

Ekuma, I. C., Ndubuka, G. I., Azeez, T. O., Nosiri, O. C., Okafor, S. A., Ekedigwe, M. C., Otuu, C. A., & Onwukamuche, C. K. (2023) · Engineering and Technology Journal · doi:10.47191/etj/v8i2.01 · Cited by 1 · see citing works

Correlação entre Ecocardiografia POCUS Guiada por Telemedicina e Dados In Situ: Estudo Piloto em uma Região Remota no Brasil

Rompkoski, J., Accorsi, T. A. D., Amaral, B. D. R., de Freitas, C. B., Moreira, F. T., Kohler, K. F., Lima, K. D. A., Morbeck, R. A., & Pedrotti, C. H. S. (2024) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20230110 · Cited by 1 · see citing works

Correlation of Telemedicine-guided POCUS Echocardiography and In Situ Data: Pilot Study in a Remote Area in Brazil

Rompkoski, J., Accorsi, T. A. D., Amaral, B. D. R., de Freitas, C. B., Moreira, F. T., Kohler, K. F., Lima, K. D. A., Morbeck, R. A., & Pedrotti, C. H. S. (2024) · ARQUIVOS BRASILEIROS DE CARDIOLOGIA - IMAGEM CARDIOVASCULAR · doi:10.36660/abcimg.20230110i · Cited by 1 · see citing works

Artificial Intelligence in Cardiovascular Healthcare

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Artificial Intelligence-Based Point-of-Care Lung Ultrasound for Screening COVID-19 Pneumoniae: Comparison with CT ScansPreprint

Kuroda, Y., Kaneko, T., Yoshikawa, H., Uchiyama, S., Nagata, Y., Matsushita, Y., Hiki, M., Minamino, T., Takahashi, K., Daida, H., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4138300

Heart Rate Measurement on Smartphone using Cardiography: A Scoping Review

Rahman, T., Krathu, W., & Arpnikanondt, C. (2023) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst57286.2023.10086930

Teleassessment Methods in Cardiopulmonary Rehabilitation

Aktan, R., & Yakut, H. (2024) · TELe-Health · doi:10.1007/978-3-031-69080-8_5

Disease Screening in Healthcare and Pandemic Outbreak

Singh, B., Jermsittiparsert, K., Lal, S., & Arora, M. K. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-6190-0.ch018

Line Up Disease Screening in Health and Pandemic Outbreak Controlling

Singh, B. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-6190-0.ch019

Enhancing Scientific Rigor With Editorial Roles and AI

Papadopoulou, P., & Lytras, M. D. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-4422-4.ch003

Artificial Intelligence and Machine Learning in Telemedicine: Transforming Remote Healthcare

Khadse, S., Verma, P., & Lande, V. (2025) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10968984

Additional file 3 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321569

Additional file 11 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321557

Additional file 5 of Pre-clinical medical student cardiac point-of-care ultrasound curriculum based on the American Society of Echocardiography recommendations: a pilot and feasibility study

Jujo, S., Lee-Jayaram, J. J., Sakka, B. I., Nakahira, A., Kataoka, A., Izumo, M., Kusunose, K., Athinartrattanapong, N., Oikawa, S., & Berg, B. W. (2021) · Figshare · doi:10.6084/m9.figshare.16621038

Additional file 9 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321575

Additional file 1 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321566

Additional file 4 of Pre-clinical medical student cardiac point-of-care ultrasound curriculum based on the American Society of Echocardiography recommendations: a pilot and feasibility study

Jujo, S., Lee-Jayaram, J. J., Sakka, B. I., Nakahira, A., Kataoka, A., Izumo, M., Kusunose, K., Athinartrattanapong, N., Oikawa, S., & Berg, B. W. (2021) · Figshare · doi:10.6084/m9.figshare.16621035

Additional file 13 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321563

Additional file 12 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321560

Additional file 10 of Medical student medium-term skill retention following cardiac point-of-care ultrasound training based on the American Society of Echocardiography curriculum framework

Jujo, S., Sakka, B. I., Lee-Jayaram, J. J., Kataoka, A., Izumo, M., Kusunose, K., Nakahira, A., Oikawa, S., Kataoka, Y., & Berg, B. W. (2022) · Figshare · doi:10.6084/m9.figshare.21321554

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