Skip to main content

Advertisement

SpringerLink
Log in

Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing

  • Review Article
  • Published:
Journal of Echocardiography Aims and scope Submit manuscript

A Letter to the Editor to this article was published on 18 August 2023

Abstract

Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report generation. In the field of echocardiography, AI offers the potential to enhance accuracy, speed up reporting, and reduce the workload of physicians. This is an advantage because, compared to computed tomography and magnetic resonance imaging, echocardiograms tend to exhibit higher observer variability in interpretation. This review takes a comprehensive viewpoint at AI-based reporting systems and their application in echocardiography, emphasizing the need for automated diagnoses. The integration of natural language processing (NLP) technologies, including ChatGPT, could provide revolutionary advancements. One of the exciting prospects of AI integration is its potential to accelerate reporting, thereby improving patient outcomes and access to treatment, while also mitigating physician burnout. However, AI introduces new challenges like ensuring data quality, managing potential over-reliance on AI, addressing legal and ethical concerns, and balancing significant costs against benefits. As we navigate these complexities, it's important for cardiologists to stay updated with AI advancements and learn to utilize them effectively. AI has the potential to be integrated into daily clinical practice, becoming a valuable tool for healthcare professionals dealing with heart diseases, provided it's approached with careful consideration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Kusunose K, Okushi Y, Okayama Y, Zheng R, Nakai M, Sumita Y, Ise T, Yamaguchi K, Yagi S, Fukuda D, Yamada H, Soeki T, Wakatsuki T, Sata M. Use of echocardiography and heart failure in-hospital mortality from registry data in Japan. J Cardiovasc Dev Dis. 2021;8:124.

    PubMed  PubMed Central  Google Scholar 

  2. Alsharqi M, Woodward W, Mumith J, Markham D, Upton R, Leeson P. Artificial intelligence and echocardiography. Echo Res Pract. 2018;5:R115–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sistrom CL, Langlotz CP. A framework for improving radiology reporting. J Am Coll Radiol. 2005;2:159–67.

    Article  PubMed  Google Scholar 

  4. Bell SK, Delbanco T, Elmore JG, Fitzgerald PS, Fossa A, Harcourt K, Leveille SG, Payne TH, Stametz RA, Walker J, DesRoches CM. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw Open. 2020;3: e205867.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mbakwe AB, Lourentzou I, Celi LA, Mechanic OJ, Dagan A. ChatGPT passing USMLE shines a spotlight on the flaws of medical education. PLOS Digit Health. 2023;2: e0000205.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kasai J, Kasai Y, Sakaguchi K, Yamada Y, Radev D (2023) Evaluating gpt-4 and chatgpt on japanese medical licensing examinations. arXiv preprint arXiv:230318027 2023

  7. Sarraju A, Bruemmer D, Van Iterson E, Cho L, Rodriguez F, Laffin L. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model. JAMA. 2023;329:842–4.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kusunose K, Kashima S, Sata M. Evaluation of the accuracy of ChatGPT in answering clinical questions on the Japanese Society of Hypertension Guidelines. Circ J 2023;87:1030–3.

    Article  PubMed  Google Scholar 

  9. Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT-Reshaping medical education and clinical management. Pak J Med Sci. 2023;39:605.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kusunose K. Radiomics in echocardiography: deep learning and echocardiographic analysis. Curr Cardiol Rep. 2020;22:89.

    Article  PubMed  Google Scholar 

  11. Kusunose K, Abe T, Haga A, Fukuda D, Yamada H, Harada M, Sata M. A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images. JACC Cardiovasc Imaging. 2020;13:374–81.

    Article  PubMed  Google Scholar 

  12. Kusunose K, Haga A, Inoue M, Fukuda D, Yamada H, Sata M. Clinically feasible and accurate view classification of echocardiographic images using deep learning. Biomolecules. 2020;10:665.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kusunose K, Haga A, Yamaguchi N, Abe T, Fukuda D, Yamada H, Harada M, Sata M. Deep learning for assessment of left ventricular ejection fraction from echocardiographic images. J Am Soc Echocardiogr. 2020;33(632–635): e631.

    Google Scholar 

  14. Morita SX, Kusunose K, Haga A, Sata M, Hasegawa K, Raita Y, Reilly MP, Fifer MA, Maurer MS, Shimada YJ. Deep learning analysis of echocardiographic images to predict positive genotype in patients with hypertrophic cardiomyopathy. Front Cardiovasc Med. 2021;8: 669860.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14:86–93.

    Article  PubMed  Google Scholar 

  16. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28:31–8.

    Article  CAS  PubMed  Google Scholar 

  17. Kusunose K. Steps to use artificial intelligence in echocardiography. J Echocardiogr. 2021;19:21–7.

    Article  PubMed  Google Scholar 

  18. Ghorbani A, Ouyang D, Abid A, He B, Chen JH, Harrington RA, Liang DH, Ashley EA, Zou JY. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020;3:10.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Nagarhalli TP, Mhatre S, Patil S, Patil P. The review of natural language processing applications with emphasis on machine learning implementations 2022. In: International Conference on Electronics and Renewable Systems (ICEARS): IEEE, 2022; pp. 1353–1358.

  20. Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, Horton K, Ogunyankin KO, Palma RA, Velazquez EJ. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the american society of echocardiography. J Am Soc Echocardiogr. 2019;32:1–64.

    Article  PubMed  Google Scholar 

  21. Otto CM. Practice of clinical echocardiography e-book. Elsevier Health Sciences; 2012.

    Google Scholar 

  22. Stoean C, Stoean R, Hotoleanu M, Iliescu D, Patru C, Nagy R. An assessment of the usefulness of image pre-processing for the classification of first trimester fetal heart ultrasound using convolutional neural networks 2021. In: 25th International Conference on System Theory, Control and Computing (ICSTCC): IEEE, 2021; pp. 242–248.

  23. Kusunose K, Haga A, Inoue M, Fukuda D, Yamada H, Sata M. Clinically feasible and accurate view classification of echocardiographic images using deep learning. Biomolecules. 2020;10:665.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Teng L, Fu Z, Yao Y. Interactive translation in echocardiography training system with enhanced cycle-GAN. IEEE access. 2020;8:106147–56.

    Article  Google Scholar 

  25. Lecler A, Duron L, Soyer P. Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT. Diagn Interv Imaging. 2023;104:269–74.

    Article  PubMed  Google Scholar 

  26. Adams LC, Truhn D, Busch F, Kader A, Niehues SM, Makowski MR, Bressem KK. Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology. 2023;307:230725.

    Article  Google Scholar 

  27. Parikh JR, Van Moore A, Mead L, Bassett R, Rubin E. Prevalence of burnout of radiologists in private practice. J Am Coll Radiol. 2023 Mar:S1546-1440(23)00196-5. https://doi.org/10.1016/j.jacr.2023.01.007

  28. Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, Folio LR, Summers RM, Rubin DL, Lungren MP. Preparing medical imaging data for machine learning. Radiology. 2020;295:4–15.

    Article  PubMed  Google Scholar 

  29. Brady AP, Neri E. Artificial intelligence in radiology—ethical considerations. Diagnostics. 2020;10:231.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Group CAoRAIW. artificial intelligence in radiology. Can Assoc Radiolog J. 2019;70:107–18.

    Google Scholar 

  31. Panch T, Mattie H, Celi LA. The “inconvenient truth” about AI in healthcare. NPJ Digit Med. 2019;2:77.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was partially supported by grants from JSPS Kakenhi Grants (Number 23K07509 to K. Kusunose) and the Japan Agency for Medical Research and Development (AMED, JP22uk1024007 to K.K.).

Author information

Authors and Affiliations

  1. Department of Cardiovascular Medicine, Nephrology, and Neurology, Graduate School of Medicine, University of the Ryukyus, 207 Uehara, Nishihara Town, Okinawa, Japan

    Kenya Kusunose

Authors
  1. Kenya Kusunose
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenya Kusunose.

Ethics declarations

Conflict of interest

Kenya Kusunose declares that they have no conflict of interest.

Human rights statements and informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later revisions. Informed consent was obtained from all patients for being included in the study.

Informed consent

Informed consent was not obtained from the patients because of not patient study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kusunose, K. Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing. J Echocardiogr 21, 99–104 (2023). https://doi.org/10.1007/s12574-023-00611-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12574-023-00611-1

Keywords

Search

Navigation