Chamsi-Pasha, M. A., Sengupta, P. P. & Zoghbi, W. A. Handheld echocardiography: Present state and future views. Circulation 136, 2178–2188 (2017).
Google Scholar
Kirkpatrick, J. N. et al. Suggestions for echocardiography laboratories collaborating in cardiac level of care cardiac ultrasound (POCUS) and demanding care echocardiography coaching: Report from the American society of echocardiography. J. Am. Soc. Echocardiogr. 33, 409–422 (2020).
Google Scholar
Johri, A. M. et al. Cardiac point-of-care ultrasound: State-of-the-art in medical faculty training. J. Am. Soc. Echocardiogr. 31, 749–760 (2018).
Google Scholar
Blanco, P. & Volpicelli, G. Frequent pitfalls in point-of-care ultrasound: A sensible information for emergency and demanding care physicians. Crit. Ultrasound J 8, 15 (2016).
Google Scholar
Kaneko, T. et al. Effectiveness of real-time tele-ultrasound for echocardiography in resource-limited medical groups. J. Echocardiogr. 20, 16–23 (2022).
Google Scholar
Ohte, N. et al. JCS 2021 guideline on the scientific utility of echocardiography. Circ. J. 86, 2045–2119 (2022).
Google Scholar
Heidenreich, P. A. et al. 2022 AHA/ACC/HFSA guideline for the administration of coronary heart failure: A report of the American School of Cardiology/American Coronary heart Affiliation Joint Committee on scientific apply tips. J. Am. Coll. Cardiol. 79, e263–e421 (2022).
Google Scholar
Thavendiranathan, P. et al. Reproducibility of echocardiographic methods for sequential evaluation of left ventricular ejection fraction and volumes: Utility to sufferers present process most cancers chemotherapy. J. Am. Coll. Cardiol. 61, 77–84 (2013).
Google Scholar
Kagiyama, N., Shrestha, S., Farjo, P. D. & Sengupta, P. P. Synthetic intelligence: Sensible primer for scientific analysis in heart problems. J. Am. Coronary heart Assoc. 8, e012788 (2019).
Google Scholar
Kusunose, Okay. Revolution of echocardiographic reporting: The brand new period of synthetic intelligence and pure language processing. J. Echocardiogr. 21, 99–104 (2023).
Google Scholar
Komuro, J., Kusumoto, D., Hashimoto, H. & Yuasa, S. Machine studying in cardiology: Scientific utility and primary analysis. J. Cardiol. 82, 128–133 (2023).
Google Scholar
Nakamura, T. & Sasano, T. Synthetic intelligence and cardiology: Present standing and perspective. J. Cardiol. 79, 326–333 (2022).
Google Scholar
Tamura, Y., Nomura, A., Kagiyama, N., Mizuno, A. & Node, Okay. Digitalomics, digital intervention, and designing future: The subsequent frontier in cardiology. J. Cardiol. 83, 318–322 (2024).
Google Scholar
Tromp, J. et al. Automated interpretation of systolic and diastolic operate on the echocardiogram: A multicohort research. Lancet Digit. Well being 4, e46–e54 (2022).
Google Scholar
Leclerc, S. et al. Deep studying for segmentation utilizing an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38, 2198–2210 (2019).
Google Scholar
Tromp, J. et al. A proper validation of a deep learning-based automated workflow for the interpretation of the echocardiogram. Nat. Commun. 13, 6776 (2022).
Google Scholar
He, B. et al. Blinded, randomized trial of sonographer versus AI cardiac operate evaluation. Nature 616, 520–524 (2023).
Google Scholar
Fitzgibbon, J. B., Lovallo, E., Escajeda, J., Radomski, M. A. & Martin-Gill, C. Feasibility of out-of-hospital cardiac arrest ultrasound by EMS physicians. Prehosp. Emerg. Care 23, 297–303 (2019).
Google Scholar
Le, M. T. et al. Comparability of 4 handheld point-of-care ultrasound gadgets by skilled customers. Ultrasound J. 14, 27 (2022).
Google Scholar
Hathaway, Q. A. et al. Ultrasonic texture options for assessing cardiac transforming and dysfunction. J. Am. Coll. Cardiol. 80, 2187–2201 (2022).
Google Scholar
Kuroda, Y. et al. Synthetic intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparability with CT scans. PLoS ONE 18, e0281127 (2023).
Google Scholar
Papadopoulou, S. L., Sachpekidis, V., Kantartzi, V., Styliadis, I. & Nihoyannopoulos, P. Scientific validation of a synthetic intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in actual time by a novel handheld ultrasound gadget. Eur. Coronary heart J. Digit. Well being 3, 29–37 (2022).
Google Scholar
Sengupta, P. P. et al. Proposed necessities for cardiovascular imaging-related machine studying analysis (PRIME): A guidelines: Reviewed by the American School of Cardiology Healthcare Innovation Council. JACC Cardiovasc. Imaging 13, 2017–2035 (2020).
Google Scholar
Kagiyama, N., Tokodi, M. & Sengupta, P. P. Machine studying in cardiovascular imaging. Coronary heart Fail. Clin. 18, 245–258 (2022).
Google Scholar
Narang, A. et al. Utility of a deep-learning algorithm to information novices to accumulate echocardiograms for restricted diagnostic use. JAMA Cardiol. 6, 624–632 (2021).
Google Scholar
Pasdeloup, D. et al. Actual-time echocardiography steerage for optimized apical normal views. Ultrasound Med. Biol. 49, 333–346 (2023).
Google Scholar
