Future Horizons: The Potential Role of Artificial Intelligence in Cardiology
<p>Number of publications related to artificial intelligence in cardiology from 2010 to 2023—adapted from [<a href="#B9-jpm-14-00656" class="html-bibr">9</a>].</p> "> Figure 2
<p>Relationship between AI, ML, and DL—adaptation from [<a href="#B15-jpm-14-00656" class="html-bibr">15</a>].</p> ">
Abstract
:1. Introduction
1.1. Terminology
1.1.1. Machine Learning (ML)
1.1.2. Deep Learning (DL)
1.1.3. Artificial Neural Networks (ANNs)
1.1.4. Convolutional Neural Networks (CNNs)
2. Materials and Methods
3. Results
3.1. Electrocardiography (ECG)
3.1.1. Echocardiography
- Assessing and monitoring the left ventricular systolic and diastolic function;
- Evaluation of the right ventricular function;
- Evaluation and quantification of the cardiac chamber size;
- Assessing the functional significance of a valvular lesion and the evaluation of the prosthetic valve structure and function;
- Identification of the cardiac source of embolism and the evaluation of the cardiac masses;
- Evaluation of pericardial diseases [58].
3.1.2. Coronary Angiography
3.1.3. Cardiac Computed Angiography
3.1.4. Computed Tomography
3.1.5. Cardiac MRI
4. Discussions
4.1. Challenges in AI Implementation
4.2. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Flora, G.D.; Nayak, M.K. A Brief Review of Cardiovascular Diseases, Associated Risk Factors and Current Treatment Regimes. Curr. Pharm. Des. 2019, 25, 4063–4084. [Google Scholar] [CrossRef]
- Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update from the GBD 2019 Study. J. Am. Coll. Cardiol. 2020, 76, 2982–3021. [Google Scholar] [CrossRef]
- Haq, I.U.; Chhatwal, K.; Sanaka, K.; Xu, B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc. Health Risk Manag. 2022, 18, 517. [Google Scholar] [CrossRef]
- Amisha; Malik, P.; Pathania, M.; Rathaur, V.K. Overview of artificial intelligence in medicine. J. Fam. Med. Prim. Care 2019, 8, 2328. [Google Scholar] [CrossRef]
- Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metabolism 2017, 69S, S36–S40. [Google Scholar] [CrossRef]
- Poalelungi, D.G.; Musat, C.L.; Fulga, A.; Neagu, M.; Neagu, A.I.; Piraianu, A.I.; Fulga, I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J. Pers. Med. 2023, 13, 1214. [Google Scholar] [CrossRef]
- Xu, N.; Yang, D.; Arikawa, K.; Bai, C. Application of artificial intelligence in modern medicine. Clin. eHealth 2023, 6, 130–137. [Google Scholar] [CrossRef]
- Fogel, A.L.; Kvedar, J.C. Artificial intelligence powers digital medicine. NPJ Digit. Med. 2018, 1, 5. [Google Scholar] [CrossRef]
- Zhang, J.; Zhang, J.; Jin, J.; Jiang, X.; Yang, L.; Fan, S.; Zhang, Q.; Chi, M. Artificial intelligence applied in cardiovascular disease: A bibliometric and visual analysis. Front. Cardiovasc. Med. 2024, 11, 1323918. [Google Scholar] [CrossRef]
- Noorbakhsh-Sabet, N.; Zand, R.; Zhang, Y.; Abedi, V. Artificial Intelligence Transforms the Future of Healthcare. Am. J. Med. 2019, 132, 795. [Google Scholar] [CrossRef]
- Bostrom, N. How Long Before Superintelligence? Int. J. Future Stud. 1998, 2, 11–30. [Google Scholar]
- Sun, K.; Roy, A.; Tobin, J.M. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research. J. Crit. Care 2024, 82, 154792. [Google Scholar] [CrossRef]
- Chakraborty, C.; Bhattacharya, M.; Pal, S.; Lee, S.-S. From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Curr. Res. Biotechnol. 2024, 7, 100164. [Google Scholar] [CrossRef]
- Botvinick, M.; Ritter, S.; Wang, J.X.; Kurth-Nelson, Z.; Blundell, C.; Hassabis, D. Reinforcement Learning, Fast and Slow. Trends Cogn. Sci. 2019, 23, 408–422. [Google Scholar] [CrossRef]
- Jakhar, D.; Kaur, I. Artificial intelligence, machine learning and deep learning: Definitions and differences. Clin. Exp. Dermatol. 2020, 45, 131–132. [Google Scholar] [CrossRef]
- Piccialli, F.; Di Somma, V.; Giampaolo, F.; Cuomo, S.; Fortino, G. A survey on deep learning in medicine: Why, how and when? Inf. Fusion. 2021, 66, 111–137. [Google Scholar] [CrossRef]
- Dongare, A.D.; Kharde, R.R.; Kachare, A.D. Introduction to Artificial Neural Network. Certif. Int. J. Eng. Innov. Technol. (IJEIT) 2008, 9001, 2277–3754. [Google Scholar]
- Micheli-Tzanakou, E. Artificial Neural Networks: Definitions, Methods, Applications. In Supervised and Unsupervised Pattern Recognition; CRC Press: Boca Raton, FL, USA, 2017; pp. 61–78. [Google Scholar]
- Zhou, S.K.; Rueckert, D.; Fichtinger, G. (Eds.) Handbook of Medical Image Computing and Computer Assisted Intervention; Academic Press: Cambridge, MA, USA, 2020. [Google Scholar]
- Berezsky, O.; Liashchynskyi, P.; Pitsun, O.; Izonin, I. Synthesis of Convolutional Neural Network architectures for biomedical image classification. Biomed. Signal Process Control 2024, 95, 106325. [Google Scholar] [CrossRef]
- Piraianu, A.I.; Fulga, A.; Musat, C.L.; Ciobotaru, O.R.; Poalelungi, D.G.; Stamate, E.; Ciobotaru, O.; Fulga, I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics 2023, 13, 2992. [Google Scholar] [CrossRef]
- Herman, R.; Meyers, H.P.; Smith, S.W.; Bertolone, D.T.; Leone, A.; Bermpeis, K.; Viscusi, M.M.; Belmonte, M.; Demolder, A.; Boza, V.; et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur. Heart J. Digit. Health 2023, 5, 123–133. [Google Scholar] [CrossRef]
- Nogimori, Y.; Sato, K.; Takamizawa, K.; Ogawa, Y.; Tanaka, Y.; Shiraga, K.; Masuda, H.; Matsui, H.; Kato, M.; Daimon, M.; et al. Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram. Int. J. Cardiol. 2024, 406, 132019. [Google Scholar] [CrossRef]
- Hillis, J.; Bizzo, B.C.; Mercaldo, S.; Ghatak, A.; Macdonald, A.; Halle, M.; Schultz, A.; L’Italien, E.; Tam, V.; Awad, A.; et al. Detection of Hypertrophic Cardiomyopathy on Electrocardiogram Using Artificial Intelligence. J. Am. Coll. Cardiol. 2024, 83, 2609. [Google Scholar] [CrossRef]
- Haimovich, J.S.; Diamant, N.; Khurshid, S.; Di Achille, P.; Reeder, C.; Friedman, S.; Singh, P.; Spurlock, W.; Ellinor, P.T.; Philippakis, A.; et al. Artificial intelligence–enabled classification of hypertrophic heart diseases using electrocardiograms. Cardiovasc. Digit. Health J. 2023, 4, 48–59. [Google Scholar] [CrossRef]
- Harmon, D.M.; Mangold, K.; Baez Suarez, A.; Scott, C.G.; Murphree, D.H.; Malik, A.; Attia, Z.I.; Lopez-Jimenez, F.; Friedman, P.A.; Dispenzieri, A.; et al. Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis. JACC Adv. 2023, 2, 100612. [Google Scholar] [CrossRef]
- Butler, L.; Karabayir, I.; Kitzman, D.W.; Alonso, A.; Tison, G.H.; Chen, L.Y.; Chang, P.P.; Clifford, G.; Soliman, E.Z.; Akbilgic, O. A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction. Cardiovasc. Digit. Health J. 2023, 4, 183–190. [Google Scholar] [CrossRef]
- Awasthi, S.; Sachdeva, N.; Gupta, Y.; Anto, A.G.; Asfahan, S.; Abbou, R.; Bade, S.; Sood, S.; Hegstrom, L.; Vellanki, N.; et al. Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG. eClinicalMedicine 2023, 65, 102259. [Google Scholar] [CrossRef]
- Lee, Y.H.; Hsieh, M.T.; Chang, C.C.; Tsai, Y.L.; Chou, R.H.; Lu, H.H.S.; Huang, P.-H. Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm. Atherosclerosis 2023, 381, 117238. [Google Scholar] [CrossRef]
- Valente Silva, B.; Marques, J.; Nobre Menezes, M.; Oliveira, A.L.; Pinto, F.J. Artificial intelligence-based diagnosis of acute pulmonary embolism: Development of a machine learning model using 12-lead electrocardiogram. Rev. Port. Cardiol. 2023, 42, 643–651. [Google Scholar] [CrossRef]
- Sau, A.; Ibrahim, S.; Kramer, D.B.; Waks, J.W.; Qureshi, N.; Koa-Wing, M.; Keene, D.; Malcolme-Lawes, L.; Lefroy, D.C.; Linton, N.W.; et al. Artificial intelligence–enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia. Cardiovasc. Digit. Health J. 2023, 4, 60–67. [Google Scholar] [CrossRef]
- Shimojo, M.; Inden, Y.; Yanagisawa, S.; Suzuki, N.; Tsurumi, N.; Watanabe, R.; Nakagomi, T.; Okajima, T.; Suga, K.; Tsuji, Y.; et al. A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins. J. Cardiovasc. Electrophysiol. 2023, 34, 627–637. [Google Scholar] [CrossRef]
- Shiokawa, N.; Izumo, M.; Shimamura, T.; Kurosaka, Y.; Sato, Y.; Okamura, T.; Akashi, Y.J. Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice. J. Clin. Med. 2024, 13, 1861. [Google Scholar] [CrossRef]
- Sveric, K.M.; Ulbrich, S.; Dindane, Z.; Winkler, A.; Botan, R.; Mierke, J.; Trausch, A.; Heidrich, F.; Linke, A. Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging. Int. J. Cardiol. 2024, 394, 131383. [Google Scholar] [CrossRef]
- Slivnick, J.A.; Gessert, N.T.; Cotella, J.I.; Oliveira, L.; Pezzotti, N.; Eslami, P.; Sadeghi, A.; Wehle, S.; Prabhu, D.; Waechter-Stehle, I.; et al. Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers. J. Am. Soc. Echocardiogr. 2024. [CrossRef]
- Kampaktsis, P.N.; Bohoran, T.A.; Lebehn, M.; McLaughlin, L.; Leb, J.; Liu, Z.; Moustakidis, S.; Siouras, A.; Singh, A.; Hahn, R.T.; et al. An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy. Echocardiography 2024, 41, e15719. [Google Scholar] [CrossRef]
- Murayama, M.; Sugimori, H.; Yoshimura, T.; Kaga, S.; Shima, H.; Tsuneta, S.; Mukai, A.; Nagai, Y.; Yokoyama, S.; Nishino, H.; et al. Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension. Echocardiography 2024, 41, e15812. [Google Scholar] [CrossRef]
- Hsia, B.C.; Lai, A.; Singh, S.; Samtani, R.; Bienstock, S.; Liao, S.; Stern, E.; LaRocca, G.; Sanz, J.; Lerakis, S.; et al. Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared with Cardiac Magnetic Resonance Imaging. J. Am. Soc. Echocardiogr. 2023, 36, 967–977. [Google Scholar] [CrossRef]
- Anand, V.; Weston, A.D.; Scott, C.G.; Kane, G.C.; Pellikka, P.A.; Carter, R.E. Machine Learning for Diagnosis of Pulmonary Hypertension by Echocardiography. Mayo Clin. Proc. 2024, 99, 260–270. [Google Scholar] [CrossRef]
- Oikonomou, E.K.; Holste, G.; Yuan, N.; Coppi, A.; McNamara, R.L.; Haynes, N.A.; Vora, A.N.; Velazquez, E.J.; Li, F.; Menon, V.; et al. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol. 2024, 9, 534–544. [Google Scholar] [CrossRef]
- Krishna, H.; Desai, K.; Slostad, B.; Bhayani, S.; Arnold, J.H.; Ouwerkerk, W.; Hummel, Y.; Lam, C.S.; Ezekowitz, J.; Frost, M.; et al. Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography. J. Am. Soc. Echocardiogr. 2023, 36, 769–777. [Google Scholar] [CrossRef]
- Guo, Y.; Xia, C.; Zhong, Y.; Wei, Y.; Zhu, H.; Ma, J.; Li, G.; Meng, X.; Yang, C.; Wang, X.; et al. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed. Eng. Online 2023, 22, 44. [Google Scholar] [CrossRef]
- Molenaar, M.A.; Bouma, B.J.; Asselbergs, F.W.; Verouden, N.J.; Selder, J.L.; Chamuleau, S.A.J.; Schuuring, M.J. Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome. Eur. Heart J. Digit. Health 2024, 5, 170–182. [Google Scholar] [CrossRef]
- Lu, N.; Vaseli, H.; Mahdavi, M.; Taheri Dezaki, F.; Luong, C.; Yeung, D.; Gin, K.; Tsang, M.; Nair, P.; Jue, J.; et al. Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach. Diseases 2024, 12, 35. [Google Scholar] [CrossRef]
- Brown, K.; Roshanitabrizi, P.; Rwebembera, J.; Okello, E.; Beaton, A.; Linguraru, M.G.; Sable, C.A. Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation. J. Am. Heart Assoc. 2024, 13, e031257. [Google Scholar] [CrossRef]
- Steffner, K.R.; Christensen, M.; Gill, G.; Bowdish, M.; Rhee, J.; Kumaresan, A.; He, B.; Zou, J.; Ouyang, D. Deep learning for transesophageal echocardiography view classification. Sci. Rep. 2024, 14, 11. [Google Scholar] [CrossRef]
- In Kim, Y.; Roh, J.H.; Kweon, J.; Kwon, H.; Chae, J.; Park, K.; Lee, J.-H.; Jeong, J.-O.; Kang, D.-Y.; Lee, P.H.; et al. Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning. Int. J. Cardiol. 2024, 405, 131945. [Google Scholar] [CrossRef]
- Rinehart, S.; Raible, S.J.; Ng, N.; Mullen, S.; Huey, W.; Rogers, C.; Pursnani, A. Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study. J. Soc. Cardiovasc. Angiogr. Interv. 2024, 3, 101296. [Google Scholar] [CrossRef]
- Omori, H.; Matsuo, H.; Fujimoto, S.; Sobue, Y.; Nozaki, Y.; Nakazawa, G.; Takahashi, K.; Osawa, K.; Okubo, R.; Kaneko, U.; et al. Determination of lipid-rich plaques by artificial intelligence-enabled quantitative computed tomography using near-infrared spectroscopy as reference. Atherosclerosis 2023, 386, 117363. [Google Scholar] [CrossRef]
- Toggweiler, S.; Wyler von Ballmoos, M.C.; Moccetti, F.; Douverny, A.; Wolfrum, M.; Imamoglu, Z.; Mohler, A.; Gülan, U.; Kim, W.-K. A fully automated artificial intelligence-driven software for planning of transcatheter aortic valve replacement. Cardiovasc. Revascularization Med. 2024. [CrossRef]
- Salehi, M.; Maiter, A.; Strickland, S.; Aldabbagh, Z.; Karunasaagarar, K.; Thomas, R.; Lopez-Dee, T.; Capener, D.; Dwivedi, K.; Sharkey, M.; et al. Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR. Front. Cardiovasc. Med. 2024, 11, 1279298. [Google Scholar] [CrossRef]
- Ghanbari, F.; Joyce, T.; Lorenzoni, V.; Guaricci, A.I.; Pavon, A.G.; Fusini, L.; Andreini, D.; Rabbat, M.G.; Aquaro, G.D.; Abete, R.; et al. AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry. Radiology 2023, 307, e222239. [Google Scholar] [CrossRef]
- Kaplan Berkaya, S.; Uysal, A.K.; Sora Gunal, E.; Ergin, S.; Gunal, S.; Gulmezoglu, M.B. A survey on ECG analysis. Biomed. Signal Process Control 2018, 43, 216–235. [Google Scholar] [CrossRef]
- Ogah, O.S.; Oladapo, O.O.; Adebiyi, A.A.; Adebayo, A.K.; Aje, A.; Ojji, D.B. Electrocardiographic left ventricular hypertrophy with strain pattern: Prevalence, mechanisms and prognostic implications. Cardiovasc. J. Afr. 2008, 19, 39. [Google Scholar]
- Bornstein, A.B.; Rao, S.S.; Marwaha, K. Left Ventricular Hypertrophy. [Updated 8 August 2023]. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK557534/ (accessed on 11 June 2024).
- Savarese, G.; Becher, P.M.; Lund, L.H.; Seferovic, P.; Rosano, G.M.C.; Coats, A.J.S. Global burden of heart failure: A comprehensive and updated review of epidemiology. Cardiovasc. Res. 2023, 118, 3272–3287. [Google Scholar] [CrossRef]
- Ojha, M.K.; Wadhwani, S.; Wadhwani, A.K.; Shukla, A. Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier. Phys. Eng. Sci. Med. 2022, 45, 665–674. [Google Scholar] [CrossRef]
- Ahmed, I.; Sasikumar, N. Echocardiography Imaging Techniques. [Updated 30 July 2023]. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK572130/ (accessed on 11 June 2024).
- Vidal-Perez, R.; Grapsa, J.; Bouzas-Mosquera, A.; Fontes-Carvalho, R.; Vazquez-Rodriguez, J.M. Current role and future perspectives of artificial intelligence in echocardiography. World J. Cardiol. 2023, 15, 284. [Google Scholar] [CrossRef]
- Carabello, B.A.; Paulus, W.J. Aortic stenosis. Lancet 2009, 373, 956–966. [Google Scholar] [CrossRef]
- Ring, L.; Shah, B.N.; Bhattacharyya, S.; Harkness, A.; Belham, M.; Oxborough, D.; Pearce, K.; Rana, B.S.; Augustine, D.X.; Robinson, S.; et al. Echocardiographic assessment of aortic stenosis: A practical guideline from the British Society of Echocardiography. Echo Res. Pract. 2021, 8, G19. [Google Scholar] [CrossRef]
- Stamate, E.; Piraianu, A.-I.; Ciobotaru, O.R.; Crassas, R.; Duca, O.; Fulga, A.; Grigore, I.; Vintila, V.; Fulga, I.; Ciobotaru, O.C. Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years. Diagnostics 2024, 14, 1103. [Google Scholar] [CrossRef]
- Suzuki, N.; Asano, T.; Nakazawa, G.; Aoki, J.; Tanabe, K.; Hibi, K.; Ikari, Y.; Kozuma, K. Clinical expert consensus document on quantitative coronary angiography from the Japanese Association of Cardiovascular Intervention and Therapeutics. Cardiovasc. Interv. Ther. 2020, 35, 105. [Google Scholar] [CrossRef]
- Ramjattan, N.A.; Lala, V.; Kousa, O.; Shams, P.; Makaryus, A.N. Coronary CT Angiography. [Updated 19 January 2024]. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK470279/ (accessed on 11 June 2024).
- Blanke, P.; Schoepf, U.J.; Leipsic, J.A. CT Transcatheter Aortic Valve Replacement. Radiology 2013, 269, 650–669. [Google Scholar] [CrossRef]
- Tseng, W.Y.I.; Su, M.Y.M.; Tseng, Y.H.E. Introduction to Cardiovascular Magnetic Resonance: Technical Principles and Clinical Applications. Acta Cardiol. Sin. 2016, 32, 129. [Google Scholar] [CrossRef]
- He, J.; Baxter, S.L.; Xu, J.; Xu, J.; Zhou, X.; Zhang, K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019, 25, 30. [Google Scholar] [CrossRef]
- Kolla, L.; Parikh, R.B. Uses and limitations of artificial intelligence for oncology. Cancer 2024, 130, 2101–2107. [Google Scholar] [CrossRef]
- Oh, S.; Kim, J.H.; Choi, S.W.; Lee, H.J.; Hong, J.; Kwon, S.H. Physician Confidence in Artificial Intelligence: An Online Mobile Survey. J. Med. Internet Res. 2019, 21, e12422. [Google Scholar] [CrossRef]
- Patel, V.; Shah, M. Artificial intelligence and machine learning in drug discovery and development. Intell. Med. 2022, 2, 134–140. [Google Scholar] [CrossRef]
- Busnatu, Ș.; Niculescu, A.G.; Bolocan, A.; Petrescu, G.E.D.; Păduraru, D.N.; Năstasă, I.; Lupușoru, M.; Geantă, M.; Andronic, O.; Grumezescu, A.M.; et al. Clinical Applications of Artificial Intelligence—An Updated Overview. J. Clin. Med. 2022, 11, 2265. [Google Scholar] [CrossRef]
- Chan, B. Black-box assisted medical decisions: AI power vs. ethical physician care. Med. Health Care Philos. 2023, 26, 285. [Google Scholar] [CrossRef]
- Poon, A.I.F.; Sung, J.J.Y. Opening the black box of AI-Medicine. J. Gastroenterol. Hepatol. 2021, 36, 581–584. [Google Scholar] [CrossRef]
- Durán, J.M.; Jongsma, K.R. Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J. Med. Ethics 2021, 47, 329–335. [Google Scholar] [CrossRef]
- Gerke, S.; Minssen, T.; Cohen, G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif. Intell. Healthc. 2020, 295–336. [Google Scholar] [CrossRef]
- Tang, L.; Li, J.; Fantus, S. Medical artificial intelligence ethics: A systematic review of empirical studies. Digit. Health 2023, 9. [Google Scholar] [CrossRef]
- Sana, M.K.; Hussain, Z.M.; Maqsood, M.H.; Shah, P.A. Artificial intelligence in celiac disease. Comput. Biol. Med. 2020, 125, 103996. [Google Scholar] [CrossRef]
- Vinny, P.W.; Vishnu, V.Y.; Padma Srivastava, M.V. Artificial Intelligence shaping the future of neurology practice. Med. J. Armed Forces India 2021, 77, 276–282. [Google Scholar] [CrossRef]
Paraclinical Investigation | Author | Year of Study | Application |
---|---|---|---|
ECG | Herman R. [22] | 2024 | Detection of occlusion myocardial infarction. |
Nogimori Y. [23] | 2024 | ECG-derived CNN is a novel marker of HF in children with different prognostic potential from BNP. | |
Hillis J. [24] | 2024 | Identification of hypertrophic cardiomyopathy on a 12 lead ECG. Classification of hypertrophic cardiomyopathy, cardiac amyloidosis, and echocardiographic LVH. Detection of cardiac amyloidosis. | |
Haimovich J. [25] | 2023 | ||
Harmon D. [26] | 2023 | ||
Butler L. [27] | 2023 | Early Heart Failure prediction using ECG-AI models. | |
Awasthi S. [28] | 2023 | Assessing the risk stratification of CAD. | |
Lee Y. [29] | 2023 | ||
Valente Silva B. [30] | 2023 | Diagnosis of Acute Pulmonary Embolism. | |
Sau A. [31] | 2023 | Distinguish AVRT from AVNRT. | |
Shimojo M. [32] | 2024 | Identification of the origin of outflow tract ventricular arrhythmia. | |
Echocardiography | Shiokawa N [33] | 2024 | Automatic measurements of transthoracic echocardiography. |
Sveric K. [34] | 2024 | Calculation of left ventricular ejection fraction. | |
Slivnick J. [35] | 2024 | Detection of Regional Wall Motion Abnormalities. | |
Kampaktsis P. [36] | 2024 | Quantification of the right ventricle. | |
Murayama M [37] | 2024 | Measuring the right ventricle ejection fraction. | |
Hsia B. [38] | 2023 | Assessing the parameters of right ventricular dysfunction. | |
Anand V. [39] | 2024 | Diagnosis of pulmonary hypertension. | |
Oikonomu E. [40] | 2024 | A video-based biomarker for detection of severe aortic stenosis. | |
Krinsha H. [41] | 2023 | Assessment of aortic stenosis. | |
Guo Y. [42] | 2023 | Detection of coronary artery disease. | |
Molenaar M. [43] | 2024 | Identifying high-risk chronic coronary syndrome patients. | |
Lu N. [44] | 2024 | Detection of atrial fibrillation on echocardiography without ECG. | |
Brown K. [45] | 2024 | Detecting rheumatic heart disease. | |
Steffner K. [46] | 2024 | Identification of standardized Transesophageal Echocardiography views. | |
Coronary Angiography | In Kim Y. [47] | 2024 | Quantitative assessment of coronary lesions. |
Cardiac Computed Angiography | Rinehart S. [48] | 2024 | Plaque quantification. |
Omori H. [49] | 2023 | Morphology of coronary plaque. | |
Computed Tomography | Toggweiler S. [50] | 2024 | Planning of transcatheter aortic valve replacements. |
Cardiac MRI | Salehi M. [51] | 2024 | Automated segmentation of both ventricles on CMR by an automatic tool. |
Ghanbari F. [52] | 2023 | Prediction of major arrhythmic events by analyzing cardiac MRI scar. |
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. |
© 2024 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
Patrascanu, O.S.; Tutunaru, D.; Musat, C.L.; Dragostin, O.M.; Fulga, A.; Nechita, L.; Ciubara, A.B.; Piraianu, A.I.; Stamate, E.; Poalelungi, D.G.; et al. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. J. Pers. Med. 2024, 14, 656. https://doi.org/10.3390/jpm14060656
Patrascanu OS, Tutunaru D, Musat CL, Dragostin OM, Fulga A, Nechita L, Ciubara AB, Piraianu AI, Stamate E, Poalelungi DG, et al. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. Journal of Personalized Medicine. 2024; 14(6):656. https://doi.org/10.3390/jpm14060656
Chicago/Turabian StylePatrascanu, Octavian Stefan, Dana Tutunaru, Carmina Liana Musat, Oana Maria Dragostin, Ana Fulga, Luiza Nechita, Alexandru Bogdan Ciubara, Alin Ionut Piraianu, Elena Stamate, Diana Gina Poalelungi, and et al. 2024. "Future Horizons: The Potential Role of Artificial Intelligence in Cardiology" Journal of Personalized Medicine 14, no. 6: 656. https://doi.org/10.3390/jpm14060656