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A primer on artificial intelligence for the paediatric cardiologist

Published online by Cambridge University Press:  22 June 2020

Addison Gearhart Open the ORCID record for Addison Gearhart [Opens in a new window] ,
Sharib Gaffar Open the ORCID record for Sharib Gaffar [Opens in a new window]  and
Anthony C. Chang
Addison Gearhart
Affiliation:
Boston Children’s Hospital, Boston, MA02115, USA
Sharib Gaffar
Affiliation:
Children’s Hospital of Orange County, Orange, CA92868, USA
Anthony C. Chang*
Affiliation:
Children’s Hospital of Orange County, Orange, CA92868, USA
*
Author for correspondence: Anthony C. Chang, CHOC Medical Intelligence and Innovation Institute (MI3), 1120 W. La Veta Ave., Suite 860, Orange, CA, USA. Tel: +1 425-877-9225. E-mail: [email protected]
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Abstract

The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient’s profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient’s genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

Keywords

Cardiologypaediatricsartificial intelligence
Type
Review Article
Information
Cardiology in the Young , Volume 30 , Issue 7 , July 2020 , pp. 934 - 945
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

Krisber, GKrisberg, K. Artificial Intelligence Transforms the Future of Medicine. Association of American Colleges. 2017. https://www.aamc.org/news-insights/artificial-intelligence-transforms-future-medicineGoogle Scholar
Erika, , Mukherjee, S.Tech’s Next Big Wave: Big Data Meets Biology. Fortune. 2018.Google Scholar
Johnson, KW, Torres Soto, J, Glicksberg, BS, et al.Artificial intelligence in cardiology. J Am Coll Cardiol 2018. doi:10.1016/j.jacc.2018.03.521.CrossRefGoogle ScholarPubMed
Krittanawong, C, Zhang, HJ, Wang, Z, Aydar, M, Kitai, T.Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017. doi:10.1016/j.jacc.2017.03.571.CrossRefGoogle ScholarPubMed
Benko, A, Sik Lányi, C.History of artificial intelligence. In: Encyclopedia of Information Science and Technology, 2nd edn. 2011. doi:10.4018/978-1-60566-026-4.ch276.Google Scholar
Moor, JH.The Turing test: the elusive standard of artificial intelligence. Comput Linguist 2004; 30: 115–116. doi:10.1162/089120104773633420.CrossRefGoogle Scholar
Moor, J.The Dartmouth College Artificial Intelligence Conference : the next fifty years. AI Mag (Am Assoc Artif Intell) 2006; 27: 87–91.Google Scholar
Rosenblatt, F.The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958; 65: 386–408. doi:10.1037/h0042519.CrossRefGoogle Scholar
Buchanan, BG, Feigenbaum, EA. Dendral and meta-dendral: their applications dimension. Artif Intell 1978; 11: 5–24. doi:10.1016/0004-3702(78)90010-3.CrossRefGoogle Scholar
Nakamura, Y.Sumex utility functions. Math Soc Sci 1996; 31: 39–47. doi:10.1016/0165-4896(95)00801-2.CrossRefGoogle Scholar
Lim, M. History of AI Winters. Digital Actuaries. 2018. Retrieved April 24, 2019, from https://www.actuaries.digital/2018/09/05/history-of-ai-winters/Google Scholar
Szolovits, P.Artificial Intelligence and Medicine. In: Artificial Intelligence in Medicine, 2019. doi:10.4324/9780429052071-1.CrossRefGoogle Scholar
Patel, VL, Shortliffe, EH, Stefanelli, M, et al.The coming of age of artificial intelligence in medicine. Artif Intell Med 2009; 46: 5–17. doi:10.1016/j.artmed.2008.07.017.CrossRefGoogle ScholarPubMed
Dawes, TJW, de Marvao, A, Shi, W, et al.Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology 2017. doi:10.1148/radiol.2016161315.CrossRefGoogle ScholarPubMed
Abdolmanafi, A, Duong, L, Dahdah, N, Cheriet, F.Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express 2017. doi:10.1364/boe.8.001203.CrossRefGoogle ScholarPubMed
AI-Natural Language Processing. Tutorialspoint, 2018. Retrieved May 18, 2018, from https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_processing.htmGoogle Scholar
Nath, C, Albaghdadi, MS, Jonnalagadda, SR.A natural language processing tool for large-scale data extraction from echocardiography reports. PLoS One 2016. doi:10.1371/journal.pone.0153749.CrossRefGoogle ScholarPubMed
High, R.The era of cognitive systems: an inside look at IBM Watson and how it works. Int Bus Mach Corp 2012; 1 (1): 1–14. http://www.redbooks.ibm.com/redpapers/pdfs/redp4955.pdfGoogle Scholar
Ahmed, MN, Toor, AS, O’Neil, K, Friedland, D. Cognitive computing and the future of health care. IEEE Pulse 2017. https://pulse.embs.org/may-2017/cognitive-computing-and-the-future-of-health-care/Google ScholarPubMed
Nemati, S, Ghassemi, MM, Clifford, GD. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2016. doi:10.1109/EMBC.2016.7591355.CrossRefGoogle Scholar
Shah, SJ, Katz, DH, Selvaraj, S, et al.Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 2015. doi:10.1161/CIRCULATIONAHA.114.010637.CrossRefGoogle ScholarPubMed
Eitel, I, Moeller, C, Munz, M, et al.Genome-wide association study in takotsubo syndrome – preliminary results and future directions. Int J Cardiol 2017; 236: 335–339. doi:10.1016/j.ijcard.2017.01.093.CrossRefGoogle ScholarPubMed
Jacoby, DL, DePasquale, EC, McKenna, WJ.Hypertrophic cardiomyopathy: diagnosis, risk stratification and treatment. CMAJ 2013; 185: 127–134. doi:10.1503/cmaj.120138.CrossRefGoogle ScholarPubMed
Soubrier, F, Chung, WK, Machado, R, et al.Genetics and genomics of pulmonary arterial hypertension. J Am Coll Cardiol 2013; 62. doi:10.1016/j.jacc.2013.10.035.CrossRefGoogle ScholarPubMed
Raghavendra, U, Fujita, H, Gudigar, A, et al.Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images. Biomed Signal Process Control 2018. doi:10.1016/j.bspc.2017.09.030.CrossRefGoogle Scholar
Carey, DJ, Fetterolf, SN, Davis, FD, et al.The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research. Genet Med 2016; 18: 906–913. doi:10.1038/gim.2015.187.CrossRefGoogle ScholarPubMed
Moore, P.Congenital interventions enter the era of big data: risks and rewards. J Am Coll Cardiol 2016; 67: 1336–1337. doi:10.1016/j.jacc.2016.01.040.CrossRefGoogle ScholarPubMed
Johnson, AEW, Pollard, TJ, Shen, L, et al.MIMIC-III, a freely accessible critical care care database. Sci Data 2016. doi:10.1038/sdata.2016.35.CrossRefGoogle ScholarPubMed
Hsieh, JC, Li, AH, Yang, CC.Mobile, cloud, and big data computing: contributions, challenges, and new directions in telecardiology. Int J Environ Res Public Health 2013; 10: 6131–6153. doi:10.3390/ijerph10116131.CrossRefGoogle ScholarPubMed
Wolf, MJ, Lee, EK, Nicolson, SC, et al.Rationale and methodology of a collaborative learning project in congenital cardiac care. Am Heart J 2016. doi:10.1016/j.ahj.2016.01.013.CrossRefGoogle ScholarPubMed
Diller, GP, Kempny, A, Babu-Narayan, SV., et al.Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 0019 patients. Eur Heart J 2019. doi:10.1093/eurheartj/ehy915.CrossRefGoogle Scholar
Samad, MD, Wehner, GJ, Arbabshirani, MR, et al.Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning. Eur Heart J Cardiovasc Imaging 2018. doi:10.1093/ehjci/jey003.CrossRefGoogle ScholarPubMed
Aczon, M, Ledbetter, D, Ho, L, Gunny, A, Flynn, A, Williams, J, Wetzel, R. Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks. arXiv Prepr arXiv 2017; 1701.06675: 1–18.Google Scholar
Melillo, P, Izzo, R, Orrico, A, et al.Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One 2015; 10. doi:10.1371/journal.pone.0118504.CrossRefGoogle ScholarPubMed
Weng, SF, Reps, J, Kai, J, Garibaldi, JM, Qureshi, N.Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 2017; 12. doi:10.1371/journal.pone.0174944.CrossRefGoogle ScholarPubMed
Olive, MK, Owens, GE.Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit. Transl Pediatr 2018. doi:10.21037/tp.2018.04.03.CrossRefGoogle ScholarPubMed
Chase, JG, Starfinger, C, Lam, Z, Agogue, F, Shaw, GM.Quantifying agitation in sedated ICU patients using heart rate and blood pressure. Physiol Meas 2004; 25: 1037–1051. doi:10.1088/0967-3334/25/4/020.CrossRefGoogle ScholarPubMed
Sukuvaara, T, Koski, EM, Mäkivirta, A, Kari, A. A knowledge-based alarm system for monitoring cardiac operated patients – technical construction and evaluation. Int J Clin Monit Comput 1993; 10: 117–126. http://www.ncbi.nlm.nih.gov/pubmed/8366312 10.1007/BF01142282CrossRefGoogle ScholarPubMed
Bhatikar, SR, DeGroff, C, Mahajan, RL.A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics. Artif Intell Med 2005; 33: 251–260. doi:10.1016/j.artmed.2004.07.008.CrossRefGoogle Scholar
Kucharski, D, Grochala, D, Kajor, M, Kańtoch, E.A deep learning approach for valve defect recognition in heart acoustic signal. Adv Intel Syst Comput; 2018. doi:10.1007/978-3-319-67220-5_1.Google Scholar
Latif, S, Usman, M, Rana, R, Qadir, J.Phonocardiographic sensing using deep learning for abnormal heartbeat detection. IEEE Sens J 2018. doi:10.1109/JSEN.2018.2870759.CrossRefGoogle Scholar
Zühlke, L, Myer, L, Mayosi, BM.The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching. Cardiovasc J Afr 2012; 23: 405–408. doi:10.5830/CVJA-2012-007.CrossRefGoogle ScholarPubMed
Green, EM, van Mourik, R, Wolfus, C, Heitner, SB, Dur, O, Semigran, MJ.Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor. Npj Digit Med 2019. doi:10.1038/s41746-019-0130-0.CrossRefGoogle ScholarPubMed
Rothman, SA, Laughlin, JC, Seltzer, J, et al.The diagnosis of cardiac arrhythmias: a prospective multi-center randomized study comparing mobile cardiac outpatient telemetry versus standard loop event monitoring. J Cardiovasc Electrophysiol 2007; 18: 241–247. doi:10.1111/j.1540-8167.2006.00729.x.CrossRefGoogle Scholar
Vashist, S, Schneider, E, Luong, J.Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics 2014; 4: 104–128. doi:10.3390/diagnostics4030104.CrossRefGoogle ScholarPubMed
Qardio. 2018. https://www.getqardio.com/Google Scholar
Tison, GH, Sanchez, JM, Ballinger, B, et al.Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol 2018. doi:10.1001/jamacardio.2018.0136.CrossRefGoogle ScholarPubMed
Ferdman, DJ, Liberman, L, Silver, ES.A smartphone application to diagnose the mechanism of pediatric supraventricular tachycardia. Pediatr Cardiol 2015; 36: 1452–1457. doi:10.1007/s00246-015-1185-6.CrossRefGoogle ScholarPubMed
Brasil, S, Pascoal, C, Francisco, R, dos Reis Ferreira, V, Videira, P, Valadão, G. Artificial intelligence (AI) in rare diseases: is the future brighter? Genes (Basel) 2019; 10: 978. doi:10.3390/genes10120978. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947640/CrossRefGoogle Scholar
Jiang, Y, Habibollah, S, Tilgner, K, et al.An induced pluripotent stem cell model of hypoplastic left heart syndrome (HLHS) reveals multiple expression and functional differences in HLHS-derived cardiac myocytes. Stem Cells Transl Med 2014; 3: 416–423. doi:10.5966/sctm.2013-0105.CrossRefGoogle ScholarPubMed
Han, L, Li, Y, Tchao, J, et al.Study familial hypertrophic cardiomyopathy using patient-specific induced pluripotent stem cells. Cardiovasc Res 2014; 104: 258–269. doi:10.1093/cvr/cvu205.CrossRefGoogle ScholarPubMed
Egashira, T, Yuasa, S, Suzuki, T, et al.Disease characterization using LQTS-specific induced pluripotent stem cells. Cardiovasc Res 2012; 95: 419–429. doi:10.1093/cvr/cvs206.CrossRefGoogle ScholarPubMed
Itzhaki, I, Maizels, L, Huber, I, et al.Modeling of catecholaminergic polymorphic ventricular tachycardia with patient-specific human-induced pluripotent stem cells J Am Coll Cardiol 2012; 60 (11): 990–1000. doi:10.1016/j.jacc.2012.02.066.CrossRefGoogle ScholarPubMed
Penttinen, K, Swan, H, Vanninen, S, et al.Antiarrhythmic effects of dantrolene in patients with catecholaminergic polymorphic ventricular tachycardia and replication of the responses using iPSC models. PLoS One 2015; 10(5). doi:10.1371/journal.pone.0125366.Google ScholarPubMed
Kubota, T.Stanford researchers create deep learning algorithm that could boost drug development. Stanford News 2017: 1–4.Google Scholar
Johnson, KW, Glicksberg, BS, Shameer, K, et al.A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: validation by serial intracoronary OCT imaging. EBioMedicine 2019. doi:10.1016/j.ebiom.2019.05.007.CrossRefGoogle ScholarPubMed
Lu, X, Jolly, MP, Georgescu, B, et al.Automatic view planning for cardiac MRI acquisition. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011. doi:10.1007/978-3-642-23626-6_59.Google Scholar
Wolterink, JM, Leiner, T, Viergever, MA, Išgum, I.Automatic segmentation and disease classification using cardiac cine MR images. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. doi:10.1007/978-3-319-75541-0_11.Google Scholar
Arafati, A, Hu, P, Finn, JP, Rickers, Carsten, Andrew, L.Cheng, HJ, Kheradvar, A.Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need. Cardiovasc Diagn Ther 2019; 9 (2). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6837938/ 10.21037/cdt.2019.06.09CrossRefGoogle ScholarPubMed
Hauptmann, A, Arridge, S, Lucka, F, Muthurangu, V, Steeden, JA.Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning–proof of concept in congenital heart disease. Magn Reson Med 2019. doi:10.1002/mrm.27480.CrossRefGoogle ScholarPubMed
Zhang, J, Gajjala, S, Agrawal, P, et al.Fully automated echocardiogram interpretation in clinical practice. Circulation 2018. doi:10.1161/circulationaha.118.034338.CrossRefGoogle ScholarPubMed
Sengupta, PP, Huang, YM, Bansal, M, et al.Cognitive machine-learning algorithm for cardiac imaging; a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging 2016; 9. doi:10.1161/CIRCIMAGING.115.004330.CrossRefGoogle ScholarPubMed
Narula, S, Shameer, K, Salem Omar, AM, Dudley, JT, Sengupta, PP.Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 2016. doi:10.1016/j.jacc.2016.08.062.CrossRefGoogle ScholarPubMed
Xu, L, Liu, M, Shen, Z, et al.DW-Net: a cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography. Comput Med Imaging Graph 2020. doi:10.1016/j.compmedimag.2019.101690.CrossRefGoogle ScholarPubMed
Dey, D, Slomka, PJ, Leeson, P, et al.Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol 2019. doi:10.1016/j.jacc.2018.12.054.CrossRefGoogle ScholarPubMed
Poffo, R, Toschi, AP, Pope, RB, et al.Robotic surgery in cardiology: a safe and effective procedure. Einstein (Sao Paulo) 2013; 11: 296–302. doi:10.1590/s1679-45082013000300007.CrossRefGoogle ScholarPubMed
Suematsu, Y, Mora, BN, Mihaljevic, T, Del Nido, PJ.Totally endoscopic robotic-assisted repair of patent ductus arteriosus and vascular ring in children. Ann Thorac Surg 2005. doi:10.1016/j.athoracsur.2005.05.078.CrossRefGoogle ScholarPubMed
Bacha, E, Kalfa, D.Minimally invasive paediatric cardiac surgery. Nat Rev Cardiol 2014; 11: 24–34. doi:10.1038/nrcardio.2013.168.CrossRefGoogle ScholarPubMed
Dugas, CM, Schussler, JM.Advanced technology in interventional cardiology: a roadmap for the future of precision coronary interventions. Trends Cardiovasc Med 2016; 26: 466–473. doi:10.1016/j.tcm.2016.02.003.CrossRefGoogle ScholarPubMed
Smilowitz, NR, Moses, JW, Sosa, FA, et al.Robotic-enhanced PCI compared to the traditional manual approach. J Invasive Cardiol 2014; 26: 318–321.Google ScholarPubMed
Madder, RD, VanOosterhout, SM, Jacoby, ME, et al.Percutaneous coronary intervention using a combination of robotics and telecommunications by an operator in a separate physical location from the patient: an early exploration into the feasibility of telestenting (the REMOTE-PCI study). EuroIntervention 2017. doi:10.4244/EIJ-D-16-00363.CrossRefGoogle Scholar
Chang, AC.Analytics and Algorithms, Big Data, Cognitive Computing, and Deep Learning in Medicine and Healthcare. Orange County; 2017. http://aimed-mi3.com/wp-content/uploads/2017/12/AIMed-E-Book.pdfGoogle Scholar