Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve
Abstract
:1. Introduction
2. Methodology
3. AI in Echocardiography: Current Landscape
4. Benefits and Implications
5. Potential Challenges and Ethical Considerations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sehly, A.; Jaltotage, B.; He, A.; Maiorana, A.; Ihdayhid, A.R.; Rajwani, A.; Dwivedi, G. Artificial Intelligence in Echocardiography: The Time is Now. Rev. Cardiovasc. Med. 2022, 23, 256. [Google Scholar] [CrossRef]
- Coppola, F.; Faggioni, L.; Gabelloni, M.; De Vietro, F.; Mendola, V.; Cattabriga, A.; Cocozza, M.A.; Vara, G.; Piccinino, A.; Lo Monaco, S. Human, all too human? An all-around appraisal of the “artificial intelligence revolution” in medical imaging. Front. Psychol. 2021, 12, 710982. [Google Scholar] [CrossRef] [PubMed]
- Topol, E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 2019, 25, 44–56. [Google Scholar] [CrossRef] [PubMed]
- Barry, T.; Farina, J.M.; Chao, C.-J.; Ayoub, C.; Jeong, J.; Patel, B.N.; Banerjee, I.; Arsanjani, R. The Role of Artificial Intelligence in Echocardiography. J. Imaging 2023, 9, 50. [Google Scholar] [CrossRef]
- Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J. 2019, 6, 94–98. [Google Scholar] [CrossRef] [PubMed]
- Bajwa, J.; Munir, U.; Nori, A.; Williams, B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc. J. 2021, 8, e188–e194. [Google Scholar] [CrossRef] [PubMed]
- Knuuti, J.; Wijns, W.; Saraste, A.; Capodanno, D.; Barbato, E.; Funck-Brentano, C.; Prescott, E.; Storey, R.F.; Deaton, C.; Cuisset, T. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur. Heart J. 2020, 41, 407–477. [Google Scholar] [CrossRef] [PubMed]
- Baumgartner, H.; Falk, V.; Bax, J.J.; De Bonis, M.; Hamm, C.; Holm, P.J.; Iung, B. 2017 ESC/EACTS Guidelines for the management of valvular heart disease. Eur. Heart J. 2017, 38, 2739–2791. [Google Scholar] [CrossRef]
- Steeds, R.P.; Garbi, M.; Cardim, N.; Kasprzak, J.D.; Sade, E.; Nihoyannopoulos, P.; Popescu, B.A.; Stefanidis, A.; Cosyns, B.; Monaghan, M. EACVI appropriateness criteria for the use of transthoracic echocardiography in adults: A report of literature and current practice review. Eur. Heart J. Cardiovasc. Imaging 2017, 18, 1191–1204. [Google Scholar] [CrossRef]
- Bouma, B.J.; Riezenbos, R.; Voogel, A.J.; Veldhorst, M.H.; Jaarsma, W.; Hrudova, J.; Cernohorsky, B.; Chamuleau, S.; van den Brink, R.B.A.; Breedveld, R. Appropriate use criteria for echocardiography in the Netherlands. Neth. Heart J. 2017, 25, 330–334. [Google Scholar] [CrossRef]
- Sengupta, P.P.; Adjeroh, D.A. Will artificial intelligence replace the human echocardiographer? Circulation 2018, 138, 1639–1642. [Google Scholar] [CrossRef] [PubMed]
- Galderisi, M.; Cosyns, B.; Edvardsen, T.; Cardim, N.; Delgado, V.; Di Salvo, G.; Donal, E.; Sade, L.E.; Ernande, L.; Garbi, M. Standardization of adult transthoracic echocardiography reporting in agreement with recent chamber quantification, diastolic function, and heart valve disease recommendations: An expert consensus document of the European Association of Cardiovascular Imaging. Eur. Heart J. Cardiovasc. Imaging 2017, 18, 1301–1310. [Google Scholar] [CrossRef]
- Klem, I.; Shah, D.J.; White, R.D.; Pennell, D.J.; van Rossum, A.C.; Regenfus, M.; Sechtem, U.; Schvartzman, P.R.; Hunold, P.; Croisille, P. Prognostic value of routine cardiac magnetic resonance assessment of left ventricular ejection fraction and myocardial damage: An international, multicenter study. Circ. Cardiovasc. 2011, 4, 610–619. [Google Scholar] [CrossRef] [PubMed]
- Coulter, S.A.; Campos, K. Artificial Intelligence in Echocardiography. Tex. Heart Inst. J. 2022, 49, e217671. [Google Scholar] [CrossRef] [PubMed]
- Nagata, Y.; Kado, Y.; Onoue, T.; Otani, K.; Nakazono, A.; Otsuji, Y.; Takeuchi, M. Impact of image quality on reliability of the measurements of left ventricular systolic function and global longitudinal strain in 2D echocardiography. Echo Res. Pract. 2018, 5, 27–39. [Google Scholar] [CrossRef]
- Asch, F.M.; Poilvert, N.; Abraham, T.; Jankowski, M.; Cleve, J.; Adams, M.; Romano, N. 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, e009303. [Google Scholar] [CrossRef]
- Alsharqi, M.; Woodward, W.J.; Mumith, J.A.; Markham, D.C.; Upton, R.; Leeson, P. Artificial intelligence and echocardiography. Echo Res. Pract. 2018, 5, R115–R125. [Google Scholar] [CrossRef]
- Madani, A.; Arnaout, R.; Mofrad, M.; Arnaout, R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 2018, 1, 6. [Google Scholar] [CrossRef]
- Vasile, C.M.; Bouteiller, X.P.; Avesani, M.; Velly, C.; Chan, C.; Jalal, Z.; Thambo, J.-B.; Iriart, X. Exploring the Potential of Artificial Intelligence in Pediatric Echocardiography—Preliminary Results from the First Pediatric Study Using AI Software Developed for Adults. J. Clin. Med. 2023, 12, 3209. [Google Scholar] [CrossRef]
- Behnami, D.; Liao, Z.; Girgis, H.; Luong, C.; Rohling, R.; Gin, K. Dual-view joint estimation of left ventricular ejection fraction with uncertainty modelling in echocardiograms. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2019; Lecture Notes in Computer Science; Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Eds.; Springer: Cham, Switzerland, 2019; Volume 11765, pp. 696–704. [Google Scholar]
- Medvedofsky, D.; Mor-Avi, V.; Amzulescu, M.; Fernández-Golfín, C.; Hinojar, R.; Monaghan, M.J.; Otani, K.; Reiken, J.; Takeuchi, M.; Tsang, W. Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: Multicentre validation study. Eur. Heart J. Cardiovasc. Imaging 2018, 19, 47–58. [Google Scholar] [CrossRef]
- Tsang, W.; Salgo, I.S.; Medvedofsky, D.; Takeuchi, M.; Prater, D.; Weinert, L.; Yamat, M.; Mor-Avi, V.; Patel, A.R. Transthoracic 3D echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC Cardiovasc. Imaging 2016, 9, 769–782. [Google Scholar] [CrossRef]
- Narang, A.; Mor-Avi, V.; Prado, A.; Volpato, V.; Prater, D.; Tamborini, G.; Fusini, L.; Pepi, M.; Goyal, N.; Addetia, K.; et al. Machine learning based automated dynamic quantification of left heart chamber volumes. Eur. Heart J. Cardiovasc. Imaging 2019, 20, 541–549. [Google Scholar] [CrossRef] [PubMed]
- Lang, R.M.; Badano, L.P.; Mor-Avi, V.; Afilalo, J.; Armstrong, A.; Ernande, L.; Flachskampf, F.A.; Foster, E.; Goldstein, S.A.; Kuznetsova, T.; et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J. Am. Soc. Echocardiogr. 2015, 28, 1–39.e14. [Google Scholar] [CrossRef] [PubMed]
- Knackstedt, C.; Bekkers, S.C.; Schummers, G.; Schreckenberg, M.; Muraru, D.; Badano, L.P.; Franke, A.; Bavishi, C.; Omar, A.M.S.; Sengupta, P.P. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: The FAST-EFs multicenter study. J. Am. Coll. Cardiol. 2015, 66, 1456–1466. [Google Scholar] [CrossRef] [PubMed]
- Salte, I.M.; Oestvik, A.; Smistad, E.; Melichova, D.; Nguyen, T.M.; Brunvand, H.; Edvardsen, T.; Loevstakken, L.; Grenne, B. 545 Deep learning/artificial intelligence for automatic measurement of global longitudinal strain by echocardiography. Eur. Heart J. Cardiovasc. Imaging 2020, 21 (Suppl. S1), jez319.279. [Google Scholar] [CrossRef]
- Zhang, J.; Gajjala, S.; Agrawal, P.; Tison, G.H.; Hallock, L.A.; Beussink-Nelson, L.; Lassen, M.H.; Fan, E.; Aras, M.A.; Jordan, C.; et al. Fully automated echocardiogram interpretation in clinical practice. Circulation 2018, 138, 1623–1635. [Google Scholar] [CrossRef]
- Moghaddasi, H.; Nourian, S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput. Biol. Med. 2016, 73, 47–55. [Google Scholar] [CrossRef]
- Playford, D.; Bordin, E.; Mohamad, R.; Stewart, S.; Strange, G. Enhanced diagnosis of severe aortic stenosis using artificial intelligence: A proof-of-concept study of 530,871 echocardiograms. JACC Cardiovasc. Imaging 2020, 13, 1087–1090. [Google Scholar] [CrossRef]
- Thalappillil, R.; Datta, P.; Datta, S.; Zhan, Y.; Wells, S.; Mahmood, F.; Cobey, F.C. Artificial intelligence for the measurement of the aortic valve annulus. J. Cardiothorac. Vasc. Anesth. 2020, 34, 65–71. [Google Scholar] [CrossRef]
- Kosaraju, A.; Goyal, A.; Grigorova, Y.; Makaryus, A.N. Left Ventricular Ejection Fraction; [Updated 24 April 2023]; StatPearls Publishing: Treasure Island, FL, USA, 2023. Available online: https://www.ncbi.nlm.nih.gov/books/NBK459131/ (accessed on 10 July 2023).
- Foley, T.A.; Mankad, S.V.; Anavekar, N.S.; Bonnichsen, C.R.; Morris, M.F.; Miller, T.D.; Araoz, P.A. Measuring Left Ventricular Ejection Fraction—Techniques and Potential Pitfalls. Eur. Cardiol. 2012, 8, 108–114. [Google Scholar] [CrossRef]
- Thavendiranathan, P.; Liu, S.; Verhaert, D.; Calleja, A.; Nitinunu, A.; Van Houten, T.; De Michelis, N.; Simonetti, O.; Rajagopalan, S.; Ryan, T.; et al. Feasibility, accuracy, and reproducibility of real-time full-volume 3D transthoracic echocardiography to measure LV volumes and systolic function: A fully automated endocardial contouring algorithm in sinus rhythm and atrial fibrillation. JACC Cardiovasc. Imaging 2012, 5, 239–251. [Google Scholar] [CrossRef] [PubMed]
- Salte, I.M.; Østvik, A.; Smistad, E.; Melichova, D.; Nguyen, T.M.; Karlsen, S.; Brunvand, H.; Haugaa, K.H.; Edvardsen, T.; Lovstakken, L.; et al. Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovasc. Imaging 2021, 14, 1918–1928. [Google Scholar] [CrossRef] [PubMed]
- Goto, S.; Mahara, K.; Beussink-Nelson, L.; Ikura, H.; Katsumata, Y.; Endo, J.; Gaggin, H.K.; Shah, S.J.; Itabashi, Y.; MacRae, C.A.; et al. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat. Commun. 2021, 12, 2726. [Google Scholar] [CrossRef]
- Davis, A.; Billick, K.; Horton, K.; Jankowski, M.; Knoll, P.; Marshall, J.E.; Paloma, A.; Palma, R.; Adams, D.B. Artificial intelligence and echocardiography: A primer for cardiac sonographers. J. Am. Soc. Echocardiogr. 2020, 33, 1061–1066. [Google Scholar] [CrossRef]
- Dey, D.; Slomka, P.J.; Leeson, P.; Comaniciu, D.; Shrestha, S.; Sengupta, P.P.; Marwick, T.H. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 2019, 73, 1317–1335. [Google Scholar] [CrossRef] [PubMed]
- Nielsen, A.; Hansen, M.B.; Tietze, A.; Mouridsen, K. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 2018, 49, 1394–1401. [Google Scholar] [CrossRef] [PubMed]
- Ouyang, D.; He, B.; Ghorbani, A.; Yuan, N.; Ebinger, J.; Langlotz, C.P.; Heidenreich, P.A.; Harrington, R.A.; Liang, D.H.; Ashley, E.A.; et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature 2020, 580, 252–256. [Google Scholar] [CrossRef]
- Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning deep features for discriminative localization. arXiv 2015, arXiv:151204150Z. [Google Scholar]
- Kusunose, K. Revolution of echocardiographic reporting: The new era of artificial intelligence and natural language processing. J. Echocardiogr. 2023, 21, 99–104. [Google Scholar] [CrossRef]
- Johnson, K.B.; Wei, W.Q.; Weeraratne, D.; Frisse, M.E.; Misulis, K.; Rhee, K.; Zhao, J.; Snowdon, J.L. Precision Medicine, AI, and the Future of Personalized Health Care. Clin. Transl. Sci. 2021, 14, 86–93. [Google Scholar] [CrossRef]
- Lekadir, K.; Leiner, T.; Young, A.A.; Petersen, S.E. Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging. Front. Cardiovasc. Med. 2020, 7, 137. [Google Scholar] [CrossRef]
- Lin, A.; Kolossváry, M.; Išgum, I.; Maurovich-Horvat, P.; Slomka, P.J.; Dey, D. Artificial intelligence: Improving the efficiency of cardiovascular imaging. Expert Rev. Med. Devices 2020, 17, 565–577. [Google Scholar] [CrossRef] [PubMed]
- Schuuring, M.J.; Išgum, I.; Cosyns, B.; Chamuleau, S.A.J.; Bouma, B.J. Routine Echocardiography and Artificial Intelligence Solutions. Front. Cardiovasc. Med. 2021, 8, 648877. [Google Scholar] [CrossRef] [PubMed]
- Pierre, K.; Haneberg, A.G.; Kwak, S.; Peters, K.R.; Hochhegger, B.; Sananmuang, T.; Tunlayadechanont, P.; Tighe, P.J.; Mancuso, A.; Forghani, R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin. Roentgenol. 2023, 58, 158–169. [Google Scholar] [CrossRef] [PubMed]
- Reich, C.; Meder, B. The Heart and Artificial Intelligence-How Can We Improve Medicine Without Causing Harm. Curr. Heart Fail. Rep. 2023, 20, 271–279. [Google Scholar] [CrossRef] [PubMed]
- Tenajas, R.; Miraut, D.; Illana, C.I.; Alonso-Gonzalez, R.; Arias-Valcayo, F.; Herraiz, J.L. Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning. Appl. Sci. 2023, 13, 3693. [Google Scholar] [CrossRef]
- Lim, L.J.; Tison, G.H.; Delling, F.N. Delling FN. Artificial Intelligence in Cardiovascular Imaging. Methodist DeBakey Cardiovasc. J. 2020, 16, 138–145. [Google Scholar] [CrossRef]
- Yoon, Y.E.; Kim, S.; Chang, H.J. Artificial Intelligence and Echocardiography. J. Cardiovasc. Imaging 2021, 29, 193–204. [Google Scholar] [CrossRef]
- Chen, C.; Qin, C.; Qiu, H.; Tarroni, G.; Duan, J.; Bai, W.; Rueckert, D. Deep Learning for Cardiac Image Segmentation: A Review. Front. Cardiovasc. Med. 2020, 7, 25. [Google Scholar] [CrossRef]
- He, B.; Kwan, A.C.; Cho, J.H.; Yuan, N.; Pollick, C.; Shiota, T.; Ebinger, J.; Bello, N.A.; Wei, J.; Josan, K.; et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature 2023, 616, 520–524. [Google Scholar] [CrossRef]
- Dave, M.; Patel, N. Artificial intelligence in healthcare and education. Br. Dent. J. 2023, 234, 761–764. [Google Scholar] [CrossRef] [PubMed]
- Kossaify, A. Quality Assurance and Improvement Project in Echocardiography Laboratory: The Pivotal Importance of Organizational and Managerial Processes. Heart Views 2021, 22, 35–44. [Google Scholar] [CrossRef] [PubMed]
- Asch, F.M.; Descamps, T.; Sarwar, R.; Karagodin, I.; Singulane, C.C.; Xie, M.; Tucay, E.S.; Tude Rodrigues, A.C.; Vasquez-Ortiz, Z.Y.; Monaghan, M.J.; et al. Human versus Artificial Intelligence-Based Echocardiographic Analysis as a Predictor of Outcomes: An Analysis from the World Alliance Societies of Echocardiography COVID Study. J. Am. Soc. Echocardiogr. 2022, 35, 1226–1237.e7. [Google Scholar] [CrossRef]
- Shen, J.; Zhang, C.J.P.; Jiang, B.; Chen, J.; Song, J.; Liu, Z.; He, Z.; Wong, S.Y.; Fang, P.H.; Ming, W.K. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med. Inform. 2019, 7, e10010. [Google Scholar] [CrossRef] [PubMed]
- Barrios, J.P.; Tison, G.H. Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective. Cell Rep. Med. 2022, 3, 100869. [Google Scholar] [CrossRef]
- Ferraz, S.; Coimbra, M.; Pedrosa, J. Assisted probe guidance in cardiac ultrasound: A review. Front. Cardiovasc. Med. 2023, 10, 1056055. [Google Scholar] [CrossRef]
- Staszak, K.; Tylkowski, B.; Staszak, M. From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring. Int. J. Environ. Res. Public Health 2023, 20, 4605. [Google Scholar] [CrossRef]
- Khanna, N.N.; Maindarkar, M.A.; Viswanathan, V.; Fernandes, J.F.E.; Paul, S.; Bhagawati, M.; Ahluwalia, P.; Ruzsa, Z.; Sharma, A.; Kolluri, R.; et al. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare 2022, 10, 2493. [Google Scholar] [CrossRef]
- Available online: https://www.cdc.gov/phlp/publications/topic/hipaa.html (accessed on 15 June 2023).
- Available online: https://gdpr-info.eu (accessed on 15 June 2023).
- Giordano, C.; Brennan, M.; Mohamed, B.; Rashidi, P.; Modave, F.; Tighe, P. Accessing Artificial Intelligence for Clinical Decision-Making. Front. Digit. Health 2021, 3, 645232. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Vasile, C.M.; Iriart, X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics 2023, 13, 3137. https://doi.org/10.3390/diagnostics13193137
Vasile CM, Iriart X. Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve. Diagnostics. 2023; 13(19):3137. https://doi.org/10.3390/diagnostics13193137
Chicago/Turabian StyleVasile, Corina Maria, and Xavier Iriart. 2023. "Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve" Diagnostics 13, no. 19: 3137. https://doi.org/10.3390/diagnostics13193137