Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Overview

Journal J Clin Med

Specialty General Medicine

Date 2021 Apr 3

PMID 33808513

Citations 18

Authors

Zeynettin Akkus

Yousof H Aly

Itzhak Z Attia

Francisco Lopez-Jimenez

Adelaide M Arruda-Olson

Patricia A Pellikka

Sorin V Pislaru

Garvan C Kane

Paul A Friedman

Jae K Oh

Affiliations

Soon will be listed here.

Abstract

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

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References

Philbrick K, Yoshida K, Inoue D, Akkus Z, Kline T, Weston A . What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images. AJR Am J Roentgenol. 2018; 211(6):1184-1193. DOI: 10.2214/AJR.18.20331. View

Hinton G, Osindero S, Teh Y . A fast learning algorithm for deep belief nets. Neural Comput. 2006; 18(7):1527-54. DOI: 10.1162/neco.2006.18.7.1527. View

Jafari M, Girgis H, Van Woudenberg N, Liao Z, Rohling R, Gin K . Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training. Int J Comput Assist Radiol Surg. 2019; 14(6):1027-1037. DOI: 10.1007/s11548-019-01954-w. View

Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston A, Philbrick K . A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol. 2019; 16(9 Pt B):1318-1328. DOI: 10.1016/j.jacr.2019.06.004. View

Kusunose K, Abe T, Haga A, Fukuda D, Yamada H, Harada M . A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images. JACC Cardiovasc Imaging. 2019; 13(2 Pt 1):374-381. DOI: 10.1016/j.jcmg.2019.02.024. View

Alsharqi M, Woodward W, Mumith J, Markham D, Upton R, Leeson P . Artificial intelligence and echocardiography. Echo Res Pract. 2018; 5(4):R115-R125. PMC: 6280250. DOI: 10.1530/ERP-18-0056. View

Ghorbani A, Ouyang D, Abid A, He B, Chen J, Harrington R . Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020; 3:10. PMC: 6981156. DOI: 10.1038/s41746-019-0216-8. View

Madani A, Arnaout R, Mofrad M, Arnaout R . Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2019; 1. PMC: 6395045. DOI: 10.1038/s41746-017-0013-1. View

Li G, Yu Y . Visual Saliency Detection Based on Multiscale Deep CNN Features. IEEE Trans Image Process. 2017; 25(11):5012-5024. DOI: 10.1109/TIP.2016.2602079. View

10.

Zhang J, Gajjala S, Agrawal P, Tison G, Hallock L, Beussink-Nelson L . Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018; 138(16):1623-1635. PMC: 6200386. DOI: 10.1161/CIRCULATIONAHA.118.034338. View

11.

Dong J, Liu S, Liao Y, Wen H, Lei B, Li S . A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes. IEEE J Biomed Health Inform. 2019; 24(4):931-942. DOI: 10.1109/JBHI.2019.2948316. View

12.

Leclerc S, Smistad E, Pedrosa J, Ostvik A, Cervenansky F, Espinosa F . Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Trans Med Imaging. 2019; 38(9):2198-2210. DOI: 10.1109/TMI.2019.2900516. View

13.

Akkus Z, Ali I, Sedlar J, Agrawal J, Parney I, Giannini C . Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. J Digit Imaging. 2017; 30(4):469-476. PMC: 5537096. DOI: 10.1007/s10278-017-9984-3. View

14.

Narula S, Shameer K, Omar A, Dudley J, Sengupta P . Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol. 2016; 68(21):2287-2295. DOI: 10.1016/j.jacc.2016.08.062. View

15.

Badrinarayanan V, Kendall A, Cipolla R . SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell. 2017; 39(12):2481-2495. DOI: 10.1109/TPAMI.2016.2644615. View

16.

Abdi A, Luong C, Tsang T, Allan G, Nouranian S, Jue J . Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View. IEEE Trans Med Imaging. 2017; 36(6):1221-1230. DOI: 10.1109/TMI.2017.2690836. View

17.

Cai J, Akkus Z, Philbrick K, Boonrod A, Hoodeshenas S, Weston A . Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning. Radiol Artif Intell. 2021; 2(5):e190183. PMC: 8082409. DOI: 10.1148/ryai.2020190183. View

18.

Akkus Z, Galimzianova A, Hoogi A, Rubin D, Erickson B . Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017; 30(4):449-459. PMC: 5537095. DOI: 10.1007/s10278-017-9983-4. View

19.

Zamzmi G, Hsu L, Li W, Sachdev V, Antani S . Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions. IEEE Rev Biomed Eng. 2020; 14:181-203. PMC: 8077725. DOI: 10.1109/RBME.2020.2988295. View

20.

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J . Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Trans Med Imaging. 2017; 37(2):384-395. DOI: 10.1109/TMI.2017.2743464. View