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Published in: Current Cardiology Reports 12/2023

13-12-2023 | Artificial Intelligence | Echocardiography (JM Gardin and AH Waller, Section Editors)

The Role of Artificial Intelligence in Echocardiography: A Clinical Update

Authors: Daniel Aziz, Kameswari Maganti, Naveena Yanamala, Partho Sengupta

Published in: Current Cardiology Reports | Issue 12/2023

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Abstract

Purpose of review

In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action.

Recent findings

Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems.

Summary

AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
Literature
13.
go back to reference Manaa A, Brahimi F, Chouiref Z, Kessouri M, Amad M. Cardiovascular diseases prediction based on dense-DNN and feature selection techniques. In: Chikhi, S., Diaz-Descalzo, G., Amine, A., Chaoui, A., Saidouni, D.E., Kholladi, M.K. (eds) Modelling and implementation of complex systems. MISC 2022. Lect Notes Netw Syst. 2023;593. Springer, Cham. https://doi.org/10.1007/978-3-031-18516-8_24. Manaa A, Brahimi F, Chouiref Z, Kessouri M, Amad M. Cardiovascular diseases prediction based on dense-DNN and feature selection techniques. In: Chikhi, S., Diaz-Descalzo, G., Amine, A., Chaoui, A., Saidouni, D.E., Kholladi, M.K. (eds) Modelling and implementation of complex systems. MISC 2022. Lect Notes Netw Syst. 2023;593. Springer, Cham. https://​doi.​org/​10.​1007/​978-3-031-18516-8_​24.
15.
go back to reference Karužas A, Balčiūnas J, Fukson M, Verikas D, Matuliauskas D, Platūkis T, Vaičiulienė D, Jurgaitytė J, Kupstytė-Krištaponė N, Dirsienė R, Jaruševičius G, Šakalytė G, Plisienė J, Lesauskaitė V. Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: feasibility, accuracy, and reproducibility. Echocardiography. 2022;39(11):1439–45. https://doi.org/10.1111/echo.15475. Epub 2022 Oct 20 PMID: 36266744.CrossRefPubMed Karužas A, Balčiūnas J, Fukson M, Verikas D, Matuliauskas D, Platūkis T, Vaičiulienė D, Jurgaitytė J, Kupstytė-Krištaponė N, Dirsienė R, Jaruševičius G, Šakalytė G, Plisienė J, Lesauskaitė V. Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: feasibility, accuracy, and reproducibility. Echocardiography. 2022;39(11):1439–45. https://​doi.​org/​10.​1111/​echo.​15475. Epub 2022 Oct 20 PMID: 36266744.CrossRefPubMed
17.
go back to reference Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol. 2021;6(6):624–32. https://doi.org/10.1001/jamacardio.2021.0185. PMID: 33599681; PMCID: PMC8204203.CrossRefPubMed Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol. 2021;6(6):624–32. https://​doi.​org/​10.​1001/​jamacardio.​2021.​0185. PMID: 33599681; PMCID: PMC8204203.CrossRefPubMed
18.
go back to reference Burke DA, Corrigan N, Herlihy M, Nasaj O, Dickson J, Delaney D, Westrup J. Real world evaluation of artificial intelligence echocardiography image guidance and acquisition with novice scanners in multiple clinical settings. Eur Heart J Cardiovasc Imaging. 2022;23(Supplement_1):jeab289.011. https://doi.org/10.1093/ehjci/jeab289.011. Burke DA, Corrigan N, Herlihy M, Nasaj O, Dickson J, Delaney D, Westrup J. Real world evaluation of artificial intelligence echocardiography image guidance and acquisition with novice scanners in multiple clinical settings. Eur Heart J Cardiovasc Imaging. 2022;23(Supplement_1):jeab289.011. https://​doi.​org/​10.​1093/​ehjci/​jeab289.​011.
19.
go back to reference Singh A, Yamat M, Guile B, Mor-Avi V, Lang RM. Performance of artificial intelligence system for prescriptive acquisition guidance of transthoracic echocardiography by novice users combined with automated quantification of ejection fraction. Eur Heart J Cardiovasc Imaging. 2022;23(Supplement_1):jeab289. https://doi.org/10.1093/ehjci/jeab289. Singh A, Yamat M, Guile B, Mor-Avi V, Lang RM. Performance of artificial intelligence system for prescriptive acquisition guidance of transthoracic echocardiography by novice users combined with automated quantification of ejection fraction. Eur Heart J Cardiovasc Imaging. 2022;23(Supplement_1):jeab289. https://​doi.​org/​10.​1093/​ehjci/​jeab289.
20.
go back to reference •• He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023;616:520–4. https://doi.org/10.1038/s41586-023-05947-3. The first randomized, blinded study performed comparing AI cardiac function assessment to expert sonographer assessment. Findings from this study suggest that AI is non-inferior and faster than expert sonographer analysis paving the way for future AI lead assessments.CrossRefPubMedPubMedCentral •• He B, Kwan AC, Cho JH, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023;616:520–4. https://​doi.​org/​10.​1038/​s41586-023-05947-3. The first randomized, blinded study performed comparing AI cardiac function assessment to expert sonographer assessment. Findings from this study suggest that AI is non-inferior and faster than expert sonographer analysis paving the way for future AI lead assessments.CrossRefPubMedPubMedCentral
21.
go back to reference Peck D, Rwebembera J, Nakagaayi D, Minja NW, Ollberding NJ, Pulle J, Klein J, Adams D, Martin R, Koepsell K, Sanyahumbi A, Beaton A, Okello E, Sable C. The use of artificial intelligence guidance for rheumatic heart disease screening by novices. J Am Soc Echocardiogr. 2023;36(7):724–32. https://doi.org/10.1016/j.echo.2023.03.001. Epub 2023 Mar 9 PMID: 36906047.CrossRefPubMed Peck D, Rwebembera J, Nakagaayi D, Minja NW, Ollberding NJ, Pulle J, Klein J, Adams D, Martin R, Koepsell K, Sanyahumbi A, Beaton A, Okello E, Sable C. The use of artificial intelligence guidance for rheumatic heart disease screening by novices. J Am Soc Echocardiogr. 2023;36(7):724–32. https://​doi.​org/​10.​1016/​j.​echo.​2023.​03.​001. Epub 2023 Mar 9 PMID: 36906047.CrossRefPubMed
23.
go back to reference Asch FM, Poilvert N, Abraham T, Jankowski M, Cleve J, Adams M, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging. 2019;12(9):e009303.CrossRefPubMedPubMedCentral Asch FM, Poilvert N, Abraham T, Jankowski M, Cleve J, Adams M, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging. 2019;12(9):e009303.CrossRefPubMedPubMedCentral
24.
go back to reference Salte IM, Oestvik A, Smistad E, Melichova D, Nguyen TM, Brunvand H. et al. 545 Deep learning/artificial intelligence for automatic measurement of global longitudinal strain by echocardiography. Eur Heart J Cardiovasc Imaging. 2020;21(Suppl 1):jez319.279. Salte IM, Oestvik A, Smistad E, Melichova D, Nguyen TM, Brunvand H. et al. 545 Deep learning/artificial intelligence for automatic measurement of global longitudinal strain by echocardiography. Eur Heart J Cardiovasc Imaging. 2020;21(Suppl 1):jez319.279.
27.
go back to reference Tromp J, Seekings PJ, Hung CL, Iversen MB, Frost MJ, Ouwerkerk W, Jiang Z, Eisenhaber F, Goh RSM, Zhao H, Huang W, Ling LH, Sim D, Cozzone P, Richards AM, Lee HK, Solomon SD, Lam CSP, Ezekowitz JA. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health. 2022;4(1):e46–54. https://doi.org/10.1016/S2589-7500(21)00235-1. Epub 2021 Dec 1 PMID: 34863649.CrossRefPubMed Tromp J, Seekings PJ, Hung CL, Iversen MB, Frost MJ, Ouwerkerk W, Jiang Z, Eisenhaber F, Goh RSM, Zhao H, Huang W, Ling LH, Sim D, Cozzone P, Richards AM, Lee HK, Solomon SD, Lam CSP, Ezekowitz JA. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health. 2022;4(1):e46–54. https://​doi.​org/​10.​1016/​S2589-7500(21)00235-1. Epub 2021 Dec 1 PMID: 34863649.CrossRefPubMed
33.
go back to reference • Sengupta PP, Shrestha S, Kagiyama N, Hamirani Y, Kulkarni H, Yanamala N, Bing R, Chin CWL, Pawade TA, Messika-Zeitoun D, Tastet L, Shen M, Newby DE, Clavel MA, Pibarot P, Dweck MR. Artificial intelligence for aortic stenosis at risk international consortium. A machine-learning framework to identify distinct phenotypes of aortic stenosis severity. JACC Cardiovasc Imaging. 2021;14(9):1707–1720. https://doi.org/10.1016/j.jcmg.2021.03.020. Epub 2021 May 19. PMID: 34023273; PMCID: PMC8434951. Findings from this study demonstrated that ML models demonstrated better discrimination and reclassification in patients with aortic stenosis compared with standard-of-care AS grading systems and risk stratification phenogrouping. • Sengupta PP, Shrestha S, Kagiyama N, Hamirani Y, Kulkarni H, Yanamala N, Bing R, Chin CWL, Pawade TA, Messika-Zeitoun D, Tastet L, Shen M, Newby DE, Clavel MA, Pibarot P, Dweck MR. Artificial intelligence for aortic stenosis at risk international consortium. A machine-learning framework to identify distinct phenotypes of aortic stenosis severity. JACC Cardiovasc Imaging. 2021;14(9):1707–1720. https://​doi.​org/​10.​1016/​j.​jcmg.​2021.​03.​020. Epub 2021 May 19. PMID: 34023273; PMCID: PMC8434951. Findings from this study demonstrated that ML models demonstrated better discrimination and reclassification in patients with aortic stenosis compared with standard-of-care AS grading systems and risk stratification phenogrouping.
34.
go back to reference Bernard J, Yanamala N, Shah R, Seetharam K, Altes A, Dupuis M, Toubal O, Mahjoub H, Dumortier H, Tartar J, Salaun E, O'Connor K, Bernier M, Beaudoin J, Côté N, Vincentelli A, LeVen F, Maréchaux S, Pibarot P, Sengupta PP. Integrating echocardiography parameters with explainable artificial intelligence for data-driven clustering of primary mitral regurgitation phenotypes. JACC Cardiovasc Imaging. 2023:S1936–878X(23)00113–4. https://doi.org/10.1016/j.jcmg.2023.02.016. Epub ahead of print. PMID: 37178071. Bernard J, Yanamala N, Shah R, Seetharam K, Altes A, Dupuis M, Toubal O, Mahjoub H, Dumortier H, Tartar J, Salaun E, O'Connor K, Bernier M, Beaudoin J, Côté N, Vincentelli A, LeVen F, Maréchaux S, Pibarot P, Sengupta PP. Integrating echocardiography parameters with explainable artificial intelligence for data-driven clustering of primary mitral regurgitation phenotypes. JACC Cardiovasc Imaging. 2023:S1936–878X(23)00113–4. https://​doi.​org/​10.​1016/​j.​jcmg.​2023.​02.​016. Epub ahead of print. PMID: 37178071.
38.
go back to reference Valsaraj A, Kalmady SV, Sharma V, Frost M, Sun W, Sepehrvand N, Ong M, Equilbec C, Dyck JRB, Anderson T, Becher H, Weeks S, Tromp J, Hung CL, Ezekowitz JA, Kaul P. Development and validation of echocardiography-based machine-learning models to predict mortality. EBioMedicine. 2023;90:104479. https://doi.org/10.1016/j.ebiom.2023.104479. Epub 2023 Feb 28. PMID: 36857967; PMCID: PMC10006431. Valsaraj A, Kalmady SV, Sharma V, Frost M, Sun W, Sepehrvand N, Ong M, Equilbec C, Dyck JRB, Anderson T, Becher H, Weeks S, Tromp J, Hung CL, Ezekowitz JA, Kaul P. Development and validation of echocardiography-based machine-learning models to predict mortality. EBioMedicine. 2023;90:104479. https://​doi.​org/​10.​1016/​j.​ebiom.​2023.​104479. Epub 2023 Feb 28. PMID: 36857967; PMCID: PMC10006431.
39.
go back to reference Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging. 2020;13(9):2017–2035. https://doi.org/10.1016/j.jcmg.2020.07.015. PMID: 32912474; PMCID: PMC7953597. Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging. 2020;13(9):2017–2035. https://​doi.​org/​10.​1016/​j.​jcmg.​2020.​07.​015. PMID: 32912474; PMCID: PMC7953597.
43.
go back to reference Poldervaart JM, Langedijk M, Backus BE, et al. Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol. 2017;227:656–661 Poldervaart JM, Langedijk M, Backus BE, et al. Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol. 2017;227:656–661
51.
go back to reference Gilbert A, Marciniak M, Rodero C, Lamata P, Samset E, Mcleod K. Generating synthetic labeled data from existing anatomical models: an example with echocardiography segmentation. IEEE Trans Med Imaging. 2021 Oct;40(10):2783–2794. https://doi.org/10.1109/TMI.2021.3051806. Epub 2021 Sep 30. PMID: 33444134; PMCID: PMC8493532. Gilbert A, Marciniak M, Rodero C, Lamata P, Samset E, Mcleod K. Generating synthetic labeled data from existing anatomical models: an example with echocardiography segmentation. IEEE Trans Med Imaging. 2021 Oct;40(10):2783–2794. https://​doi.​org/​10.​1109/​TMI.​2021.​3051806. Epub 2021 Sep 30. PMID: 33444134; PMCID: PMC8493532.
52.
go back to reference • Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5(9):e2233946. https://doi.org/10.1001/jamanetworkopen.2022.33946. PMID: 36173632; PMCID: PMC9523495 . Large systematic review of machine learning interventions in healthcare. Shows data of AI usage in other modalities other than echocardiography and its robust role in the cardiovascular healthcare future. Also encompasses landmark studies in the realm of AI.CrossRefPubMedPubMedCentral • Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5(9):e2233946. https://​doi.​org/​10.​1001/​jamanetworkopen.​2022.​33946. PMID: 36173632; PMCID: PMC9523495 . Large systematic review of machine learning interventions in healthcare. Shows data of AI usage in other modalities other than echocardiography and its robust role in the cardiovascular healthcare future. Also encompasses landmark studies in the realm of AI.CrossRefPubMedPubMedCentral
55.
56.
58.
go back to reference Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. License: CC BY-NC-SA 3.0 IGO. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. License: CC BY-NC-SA 3.0 IGO.
59.
go back to reference Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P, Somani BK. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022;14(9):862322. https://doi.org/10.3389/fsurg.2022.862322. PMID: 35360424; PMCID: PMC8963864 .CrossRef Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P, Somani BK. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg. 2022;14(9):862322. https://​doi.​org/​10.​3389/​fsurg.​2022.​862322. PMID: 35360424; PMCID: PMC8963864 .CrossRef
62.
go back to reference Biancolini ME, Capellini K, Costa E, Groth C, Celi S. Fast interactive CFD evaluation of hemodynamics assisted by RBF mesh morphing and reduced order models: the case of aTAA modelling. Int J Interactive Design and Manufacturing (IJIDeM). 2020;14:1227–38.CrossRef Biancolini ME, Capellini K, Costa E, Groth C, Celi S. Fast interactive CFD evaluation of hemodynamics assisted by RBF mesh morphing and reduced order models: the case of aTAA modelling. Int J Interactive Design and Manufacturing (IJIDeM). 2020;14:1227–38.CrossRef
63.
go back to reference Sharma P, Suehling M, Flohr T, Comaniciu D. Artificial Intelligence in diagnostic imaging: status quo, challenges, and future opportunities. J Thorac Imag. 2020;35:S11–6.CrossRef Sharma P, Suehling M, Flohr T, Comaniciu D. Artificial Intelligence in diagnostic imaging: status quo, challenges, and future opportunities. J Thorac Imag. 2020;35:S11–6.CrossRef
64.
go back to reference Hirschvogel M, Jagschies L, Maier A, Wildhirt SM, Gee MW. An in silico twin for epicardial augmentation of the failing heart. Int J Numer Method Biomed Eng. 2019;35:e3233.CrossRefPubMed Hirschvogel M, Jagschies L, Maier A, Wildhirt SM, Gee MW. An in silico twin for epicardial augmentation of the failing heart. Int J Numer Method Biomed Eng. 2019;35:e3233.CrossRefPubMed
67.
Metadata
Title
The Role of Artificial Intelligence in Echocardiography: A Clinical Update
Authors
Daniel Aziz
Kameswari Maganti
Naveena Yanamala
Partho Sengupta
Publication date
13-12-2023
Publisher
Springer US
Published in
Current Cardiology Reports / Issue 12/2023
Print ISSN: 1523-3782
Electronic ISSN: 1534-3170
DOI
https://doi.org/10.1007/s11886-023-02005-2

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