The Next Frontier in Pediatric Cardiology Artificial Intelligence

被引：14

作者：

Gaffar, Sharib ^{[1
]}

Gearhart, Addison S. ^{[2
]}

Chang, Anthony C. ^{[3
]}

机构：

[1] Choc Childrens Hosp Orange Cty, UC Irvine Pediat Residency Program, 757 Westwood Plaza,Ste 5235, Los Angeles, CA 90095 USA

[2] Boston Childrens Hosp, Heart Ctr, 300 Longwood Ave, Boston, MA 02115 USA

[3] Childrens Hosp Orange Cty, Sharon Disney Lund Med Intelligence & Innovat Ins, 1120 W La Veta Ave,STE 860, Orange, CA 92868 USA

来源：

PEDIATRIC CLINICS OF NORTH AMERICA | 2020年 / 67卷 / 05期

关键词：

Artificial intelligence; Machine and deep learning; Cognitive computing; Natural language processing; Robotic process automation; PREDICTION;

D O I：

10.1016/j.pcl.2020.06.010

中图分类号：

R72 [儿科学];

学科分类号：

100202 ;

摘要：

Artificial intelligence (AI) in the last decade centered primarily around digitizing and incorporating the large volumes of patient data from electronic health records. AI is now poised to make the next step in health care integration, with precision medicine, imaging support, and development of individual health trends with the popularization of wearable devices. Future clinical pediatric cardiologists will use AI as an adjunct in delivering optimum patient care, with the help of accurate predictive risk calculators, continual health monitoring from wearables, and precision medicine. Physicians must also protect their patients’ health information from monetization or exploitation. © 2020 Elsevier Inc.

引用

页码：995 / 1009

页数：15

共 33 条

[1]

Aczon M, 2017, ARXIV170106675CSMATH

[2] Cardiovascular Event Prediction by Machine Learning The Multi-Ethnic Study of Atherosclerosis [J].

Ambale-Venkatesh, Bharath ;

Yang, Xiaoying ;

Wu, Colin O. ;

Liu, Kiang ;

Hundley, W. Gregory ;

McClelland, Robyn ;

Gomes, Antoinette S. ;

Folsom, Aaron R. ;

Shea, Steven ;

Guallar, Eliseo ;

Bluemke, David A. ;

Lima, Joao A. C. .

CIRCULATION RESEARCH, 2017, 121 (09) :1092-+

[3]

[Anonymous], 2016, COMPUT METH PROG BIO, DOI DOI 10.1016/j.cmpb.2015.12.021

[4] A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI [J].

Avendi, M. R. ;

Kheradvar, Arash ;

Jafarkhani, Hamid .

MEDICAL IMAGE ANALYSIS, 2016, 30 :108-119

[5] Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics [J].

Balthazar, Patricia ;

Harri, Peter ;

Prater, Adam ;

Safdar, Nabile M. .

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2018, 15 (03) :580-586

[6]

Chen C, 2020, FRONT CARDIOVASC MED, V7, P1

[7] Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study [J].

Dawes, Timothy J. W. ;

de Marvao, Antonio ;

Shi, Wenzhe ;

Fletcher, Tristan ;

Watson, Geoffrey M. J. ;

Wharton, John ;

Rhodes, Christopher J. ;

Howard, Luke S. G. E. ;

Gibbs, J. Simon R. ;

Rueckert, Daniel ;

Cook, Stuart A. ;

Wilkins, Martin R. ;

O'Regan, Declan P. .

RADIOLOGY, 2017, 283 (02) :381-390

[8] Automation, machine learning, and artificial intelligence in echocardiography: A brave new world [J].

Gandhi, Sumeet ;

Mosleh, Wassim ;

Shen, Joshua ;

Chow, Chi-Ming .

ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES, 2018, 35 (09) :1402-1418

[9] Congenital Heart Defects in the United States Estimating the Magnitude of the Affected Population in 2010 [J].

Gilboa, Suzanne M. ;

Devine, Owen J. ;

Kucik, James E. ;

Oster, Matthew E. ;

Riehle-Colarusso, Tiffany ;

Nembhard, Wendy N. ;

Xu, Ping ;

Correa, Adolfo ;

Jenkins, Kathy ;

Marelli, Ariane J. .

CIRCULATION, 2016, 134 (02) :101-+

[10] Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease [J].

Hauptmann, Andreas ;

Arridge, Simon ;

Lucka, Felix ;

Muthurangu, Vivek ;

Steeden, Jennifer A. .

MAGNETIC RESONANCE IN MEDICINE, 2019, 81 (02) :1143-1156

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