Aller au contenu

« L'intelligence artificielle et l'ECG » : différence entre les versions

aucun résumé des modifications
(ajout des sources)
Aucun résumé des modifications
Ligne 89 : Ligne 89 :


== Bibliographie ==
== Bibliographie ==
1.            David Louapre. Deep learning, science étonnante. 2016; Disponible sur: <nowiki>https://scienceetonnante.com/2016/04/08/le-deep-learning/</nowiki>
2.            Friberg L, Rosenqvist M, Lip GYH. Evaluation of risk stratification schemes for ischaemic stroke and bleeding in 182 678 patients with atrial fibrillation: the Swedish Atrial Fibrillation cohort study. Eur Heart J. juin 2012;33(12):1500‑10.
3.            Mittal S, Oliveros S, Li J, Barroyer T, Henry C, Gardella C. AI Filter Improves Positive Predictive Value of Atrial Fibrillation Detection by an Implantable Loop Recorder. JACC Clin Electrophysiol. août 2021;7(8):965‑75.
4.            Aksu T, Guler TE, Bozyel S, Yalin K. Usage of a new mapping algorithm to detect possible critical substrate for continuity of atrial fibrillation: fractionation mapping in preliminary experience. J Interv Card Electrophysiol Int J Arrhythm Pacing. juin 2020;58(1):29‑34.
5.            Fiorina L, Marijon E, Maupain C, Coquard C, Larnier L, Rischard J, et al. 222AI-based strategy enables faster Holter ECG analysis with equivalent clinical accuracy compared to a classical strategy. EP Eur. 1 juin 2020;22(Supplement_1):euaa162.374.
6.            Inohara T, Shrader P, Pieper K, Blanco RG, Thomas L, Singer DE, et al. Association of of Atrial Fibrillation Clinical Phenotypes With Treatment Patterns and Outcomes: A Multicenter Registry Study. JAMA Cardiol. 1 janv 2018;3(1):54‑63.
7.            Kashou. AH. A comprehensive artificial intelligence–enabled electrocardiogram interpretation program. Cardiovasc Digit Health J. 1 sept 2020;1(2):62‑70.
8.            Zhu H, Cheng C, Yin H, Li X, Zuo P, Ding J, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. Lancet Digit Health. juill 2020;2(7):e348‑57.
9.            Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh 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 Lond Engl. 7 sept 2019;394(10201):861‑7.
10.          Attia ZI, Friedman PA, Noseworthy PA, Lopez-Jimenez F, Ladewig DJ, Satam G, et al. Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs. Circ Arrhythm Electrophysiol. sept 2019;12(9):e007284.
11.          Tison GH, Zhang J, Delling FN, Deo RC. Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery. Circ Cardiovasc Qual Outcomes. sept 2019;12(9):e005289.
12.          Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, et al. Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram. J Am Coll Cardiol. 25 févr 2020;75(7):722‑33.
13.          Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, 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. 1 août 2020;13(8):e008437.
14.          Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, et al. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 1 mai 2019;4(5):428‑36.
15.          Schläpfer J, Wellens HJ. Computer-Interpreted Electrocardiograms: Benefits and Limitations. J Am Coll Cardiol. 29 août 2017;70(9):1183‑92.
819

modifications