La inteligencia artificial en el diagnóstico por imagen cardiaca: un camino lleno de retos, desafíos y trampas

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https://doi.org/10.37615/retic.v6n3a1

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Tsang W, Salgo IS, Medvedofsky D, et al. Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm. JACC Cardiovasc Imaging. 2016 Jul;9(7):769-782. doi: https://doi.org/10.1016/j.jcmg.2015.12.020 DOI: https://doi.org/10.1016/j.jcmg.2015.12.020

García-García E, González-Romero GM, Martín-Pérez EM, et al. Real-World Data and Machine Learning to Predict Cardiac Amyloidosis. Int J Environ Res Public Health. 2021 Jan 21;18(3):908. doi: https://doi.org/10.3390/ijerph18030908 DOI: https://doi.org/10.3390/ijerph18030908

Kusunose K, Abe T, Haga A, et al. A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality from Echocardiographic Images. JACC Cardiovasc Imaging. 2020 Feb;13(2 Pt 1):374-381. doi: https://doi.org/10.1016/j.jcmg.2019.02.024 DOI: https://doi.org/10.1016/j.jcmg.2019.02.024

Wifstad SV, Lovstakken L, Avdal J, et al. Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning. IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Dec;69(12):3317-3326. doi: https://doi.org/10.1109/TUFFC.2022.3218281 DOI: https://doi.org/10.1109/TUFFC.2022.3218281

Leha A, Hellenkamp K, Unsöld B, et al. A machine learning approach for the prediction of pulmonary hypertension. PLoS ONE. 2019;14(10):e0224453. https://doi.org/10.1371/journal.pone.0224453 DOI: https://doi.org/10.1371/journal.pone.0224453

Sengupta PP, Shrestha S, Kagiyama N, et al. A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity. JACC. Cardiovascular Imaging. 2021 Sep;14(9):1707-1720. doi: https://doi.org/10.1016/j.jcmg.2021.03.020 DOI: https://doi.org/10.1016/j.jcmg.2021.03.020

Skandarani Y, Lalande A, Afilalo J, et al. Generative Adversarial Networks in Cardiology. Can J Cardiol. 2022 Feb;38(2):196-203. doi: https://doi.org/10.1016/j.cjca.2021.11.003 DOI: https://doi.org/10.1016/j.cjca.2021.11.003

Koulaouzidis G, Jadczyk T, Iakovidis DK, et al. Artificial Intelligence in Cardiology-A Narrative Review of Current Status. J Clin Med. 2022 Jul;11(13):3910. doi:https://doi.org/10.3390/jcm11133910 DOI: https://doi.org/10.3390/jcm11133910

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2023-12-30

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1.
García Fernández M Ángel. La inteligencia artificial en el diagnóstico por imagen cardiaca: un camino lleno de retos, desafíos y trampas. Rev Ecocardiogr Pract Otras Tec Imag Card (RETIC) [Internet]. 30 de diciembre de 2023 [citado 22 de noviembre de 2024];6(3):I-IV. Disponible en: https://imagenretic.org/RevEcocarPract/article/view/636