. Echocardiographie, E. Abergel, A. Cohen, P. Guéret, and R. S. Roudaut, , 2006.

, European Association of Echocardiography recommendations for training, competence, and quality improvement in echocardiography, European Journal of Echocardiography, vol.8, pp.893-905, 2009.

P. Vignon, H. Mentec, S. Terre, H. Gastinne, P. Gueret et al., Diagnostic accuracy and therapeutic impact of transthoracic and transesophageal echocardiography in mechanically ventilated patients in the ICU, Chest, vol.106, pp.1829-1834, 1994.

A. Boussuges, P. Blanc, F. Molenat, H. Burnet, G. Habib et al., Evaluation of left filling pressure by transthoracic Doppler echocardiography in the intensive care unit, Crit Care Med, vol.30, pp.362-367, 2002.

F. Vargas, D. Gruson, R. Valentino, N. Bui, H. Salmi et al., Transesophageal pulsed Doppler echocardiography of pulmonary venous flow to assess left ventricular filling pressure in ventilated patients with acute respiratory distress syndrome, vol.19, pp.187-197, 2004.

B. Bouhemad, A. Nicolas-robin, A. Benois, S. Lemaire, J. P. Goarin et al., Echocardiographic Doppler assessment of pulmonary capillary wedge pressure in surgical patients with postoperative circulatory shock and acute lung injury, Anesthesiology, vol.98, pp.1091-1100

F. Lapostolle, T. Petrovic, and G. Lenoir, Usefulness of hand-held ultrasound devices in out-of-hospital diagnosis performed by emergency physicians, Am J Emerg Med, 2006.

, ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation, European Heart Journal, vol.37, pp.267-315

, Management of chest pain in the French emergency healthcare system: the prospective observational EPIDOULTHO study, European Journal of Emergency Medicine, 2017.

, Echography or auscultation. C, Shub. 2003, Can Fam Phys

A. Jost and C. , Echography in evaluating systolic murmurs of unknow cause, Am J Med, 2000.

, Auscultation versus echography in a healthy population with precordial murmur. EA, Shry, Am J Cardiol, 2001.

S. Reichlin, Initial clinical evaluation of cardiac systolic murmurs in the ed by noncardiologist, Am J Emerg Med, 2004.

, Institute for Health Metrics and Evaluation, Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 . (IHME), 2017.

B. Reményi, World Heart Federation criteria for echocardiographic diagnosis of rheumatic heart disease-an evidence-based guideline, Nat. Rev. Cardiol, pp.297-209, 2012.

, Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. SOLVD, Investigators. 1992, N Engl J Med

, ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Ponikowski, and al, European Heart Journal, vol.27, pp.2129-2200, 2016.

, Focused Cardiac Ultrasound: Recommendations from the American Society of Echocardiography

. Spencer, Journal of the American Society of Echocardiography, vol.6, pp.567-581, 2013.

, Fast Track Echo of Abdominal Aortic Aneurysm Using a Real Pocket-Ultrasound Device at Bedside: Ultraportable Echography and Abdominal Aortic Aneurysm. Dijos, Echocardiography, vol.29, pp.285-290, 2012.

. Roudaut, Intérêt de l'échographie ultraportable dans la prise en charge et l'orientation du patient en préhospitalier, 2012.

, Focused Cardiac Ultrasound in the Emergent Setting: A Consensus Statement of the American Society of Echocardiography and American College of Emergency Physicians. Labovitz, et al. 12, Journal of the American Society of Echocardiography, vol.23, 2010.

. Yates, Premier niveau de compétence pour l'échographie clinique en médecine d'urgence. Recommandations de la Société française de médecine d'urgence par consensus formalisé. SFMU, membres de la commission des référentiels de la, Annales françaises de médecine d'urgence, vol.3, pp.284-295, 2016.

C. A. Rouse, A retrospective analysis of a pediatric tele-echocardiography service to treat, triage, and reduce trans-Pacific transport, Journal of telemedecine and Telecare, 2017.

. Étude-pilote-au-togo and . Adambounou, Système de télé-expertise échographique temps réel et de télédiagnostic échographique temps différé, Médecine et santé tropicales, vol.22, pp.54-60, 2012.

P. Arbeille, Echocardiography Using a Robotic Arm and an Internet Connection, Ultrasound in Medicine & Biology, vol.10, pp.2521-2529, 2014.

, Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Haenssle, Annals of Oncology, vol.29, pp.1836-1842, 2018.

, End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Ardila, et al. 6, 2019, Nature Medicine, vol.25, pp.954-961

, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Gulshan, and al. 22, JAMA, vol.316, p.2402, 2016.

, Computing Machinery and Intelligence. Turing, A. 236, 1950, Mind, New Series, vol.59, pp.433-460

, Artificial Intelligence. Organization, World Intellectual Property, 2019.

. Krizhevsky, Communications of the ACM, vol.60, pp.84-90, 2017.

, Clinical applications of machine learning in cardiovascular disease and its relevance to cardiacimaging, Eur Heart J, 2019.

, Fully Automated Echocardiogram Interpretation in Clinical Practice: Feasibility and Diagnostic Accuracy. Zhang, et al. 16, vol.138, pp.1623-1635, 2018.

, Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert, Circulation: Cardiovascular Imaging, vol.12, 2019.

. Lieman-sifry, FastVentricle: Cardiac Segmentation with ENet, 2017, International Conference on Functional Imaging and Modeling of the heart, pp.127-138

. Litjens, State-of-the-Art Deep Learning in Cardiovascular Image Analysis, JACC: Cardiovascular Imaging, 2019.

, Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis. Zreik, et al, Medical Image Analysis, vol.44, pp.72-85, 2018.

. Gessert, Automatic plaque detection in ivoct pullbacks using convolutionel neural networks, IEEE Trans Med Imaging, 2018.

, Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch. Tison, et al. 5, vol.3, pp.409-416, 2018.

. Choi, Using recurrent neural network models for early detection of heart failure onset, Journal of the American Medical Informatics Association, 2016.

, A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data, BMC Medical Informatics and Decision Making, vol.18, issue.1, 2018.

, Analysis of Machine Learning Techniques for Heart Failure Readmissions, Circulation: Cardiovascular Quality and Outcomes, vol.6, 2016.

, Échoscopie cardiaque en médecine générale: évaluation de paramètres hémodynamiques pour le suivi des patients insuffisants cardiaques. Alexis, 2019.

. Madani, Fast and accurate view classification of echocardiograms using deep learning, NPJ Digital Medicine, vol.1, 2018.

. Gao, A fused deep learning architecture for viewpoint classification of echocardiography, Information Fusion, vol.36, 2017.

. Østvik, Recommendations for chamber quantification: A report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks, vol.45, pp.1440-1463, 2005.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, pp.1409-1556, 2014.

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, vol.53, 2015.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

. Serment-d'hippocrate,

, Au moment d'être admis à exercer la médecine, je promets et je jure d'être fidèle aux lois de l'honneur et de la probité

, Mon premier souci sera de rétablir, de préserver ou de promouvoir la santé dans tous ses éléments, physiques et mentaux, individuels et sociaux

, Je respecterai toutes les personnes, leur autonomie et leur volonté, sans aucune discrimination selon leur état ou leurs convictions. J'interviendrai pour les protéger si elles sont affaiblies, vulnérables ou menacées dans leur intégrité ou leur dignité. Même sous la contrainte

, J'informerai les patients des décisions envisagées, de leurs raisons et de leurs

, Reçu à l'intérieur des maisons, je respecterai les secrets des foyers et ma conduite ne servira pas à corrompre les moeurs

, Je ne prolongerai pas abusivement les agonies. Je ne provoquerai jamais la mort délibérément

, Je n'entreprendrai rien qui dépasse mes compétences. Je les entretiendrai et les perfectionnerai pour assurer au mieux les services qui me seront demandés

, J'apporterai mon aide à mes confrères ainsi qu'à leurs familles dans l'adversité

, Que les hommes et mes confrères m'accordent leur estime si je suis fidèle à mes promesses ; que je sois déshonoré et méprisé si j'y manque