Artificial intelligence in congenital cardiology_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XIE Wen ^1,2 , YAO Zeyang ^1,2 , QIU Hailong ² , XU Xiaowei ² ,  ZHUANG Jian ^1,2

1. Department of Cardiac Surgery, Guangdong Provincial People’s Hospital, South China University of Technology, Guangzhou, 510100, P.R. China;
2. Department of Cardiac Surgery, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510100, P.R. China;

Corresponding author：

ZHUANG Jian, Email: Zhuangjian5413@tom.com

Keywords：

Artificial intelligence; congenital heart disease; machine learning; deep learning; neural network

DOI：

10.7507/1007-4848.201911085

Video：

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Artificial intelligence belongs to the field of computer science. In the past few decades, artificial intelligence has shown broad application prospects in the medical field. With the development of computer technology in recent years, doctors and computer scientists have just begun to discover its potential for clinical application, especially in the field of congenital heart disease. Artificial intelligence now has been successfully applied to the prediction, intelligent diagnosis, medical image segmentation and recognition, clinical decision support of congenital heart disease. This article reviews the application of artificial intelligence in congenital cardiology.

Citation： XIE Wen, YAO Zeyang, QIU Hailong, XU Xiaowei, ZHUANG Jian. Artificial intelligence in congenital cardiology. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2020, 27(3): 343-353. doi: 10.7507/1007-4848.201911085 Copy

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1. Sissman NJ. Incidence of congenital heart disease. JAMA, 2001, 285(20): 2579-2580.
2. van der Linde D, Konings EE, Slager MA, et al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol, 2011, 58(21): 2241-2247.
3. 朱军, 周光萱, 代礼, 等. 1996~2000 年全国围产期先天性心脏病发生率的分析. 四川大学学报(医学版), 2004, 35(6): 875-877.
4. Mendis S, Puska P, Norrving B, et al. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organ, 2011.
5. Connor JM. Genetic disorders and the fetus. Diagnosis, prevention and treatment. J Med Genet, 1993, 30(4): 351.
6. Hoffman JI, Kaplan S. The incidence of congenital heart disease. J Am Coll Cardiol, 2002, 39(12): 1890-1900.
7. Marelli AJ, Ionescu-Ittu R, Mackie AS, et al. Lifetime prevalence of congenital heart disease in the general population from 2000 to 2010. Circulation, 2014, 130(9): 749-756.
8. Diller G, Kempny A, Alonso-Gonzalez R, et al. Survival prospects and circumstances of death in contemporary adult congenital heart disease patients under follow-up at a large tertiary centre. Circulation, 2015, 132(22): 2118-2125.
9. Yu C, Moore BM, Kotchetkova I, et al. Causes of death in a contemporary adult congenital heart disease cohort. Heart, 2018, 104(20): 1678-1682.
10. Oster ME, Strickland MJ, Mahle WT. Impact of prior hospital mortality versus surgical volume on mortality following surgery for congenital heart disease. J Thorac Cardiovasc Surg, 2011, 142(4): 882-886.
11. Apfeld JC, Kastenberg ZJ, Gibbons AT, et al. The disproportionate cost of operation and congenital anomalies in infancy. Surgery, 2019.
12. Russo CA, Elixhauser A. Hospitalizations for birth defects, 2004: Statistical Brief #24. Rockville: Agency for Healthcare Research and Quality. 2006.
13. 陈英耀, 张洁, 李军, 等. 先天性心脏病的疾病经济负担研究. 中华医院管理杂志, 2007, 23(11): 740-744.
14. 陈英耀. 我国主要出生缺陷的疾病负担和预防措施的经济学评价研究. 复旦大学, 2006.
15. Moore GE. Cramming more components onto integrated circuits. P IEEE, 1998, 86(1): 82-85.
16. Hinton GE, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527-1554.
17. Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 1997, 45(37): 870-877.
18. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
19. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys, 1943, 5(4): 115-133.
20. Hebb DO. The organization of behavior: a neuropsychological theory. Science Editions, 1962.
21. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev, 1958, 65(6): 386.
22. Widrow B, Hoff ME. Adaptive switching circuits, in neurocomputing: foundations of research. Cambridge: MIT Press. 1988. 123-134.
23. Haykin S. Neural networks: a comprehensive foundation. Prentice Hall PTR, 1994.
24. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks, 2015, 61: 85-117.
25. Bengio Y, Goodfellow I, Courville A. Deep learning. Citeseer, 2017.
26. Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell, 2013, 35(8): 1798-1828.
27. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
28. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform, 2017, 19(6): 1236-1246.
29. Najafabadi MM, Villanustre F, Khoshgoftaar TM, et al. Deep learning applications and challenges in big data analytics. J Big Data, 2015, 2(1): 1.
30. Haberl MG, Churas C, Tindall L, et al. CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods, 2018, 15(9): 677.
31. Sullivan DP, Winsnes CF, Åkesson L, et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat Biotechnol, 2018, 36(9): 820.
32. Strack R. Deep learning advances super-resolution imaging. Nat Methods, 2018, 15(6): 403.
33. Fang S, Tsao Y, Hsiao M, et al. Detection of pathological voice using cepstrum vectors: A deep learning approach. J Voice, 2019, 33(5): 634-641.
34. Chen MC, Ball RL, Yang L, et al. Deep learning to classify radiology free-text reports. Radiology, 2017, 286(3): 845-852.
35. Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Conference on empirical methods in natural language processing, 2014.
36. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA, 2017, 318(22): 2211-2223.
37. Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 2017, 124(7): 962-969.
38. Zheng Q, Delingette H, Duchateau N, et al. 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans Med Imaging, 2018, 37(9): 2137-2148.
39. Bejnordi BE, Veta M, van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22): 2199-2210.
40. Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559.
41. Zhou J, Park CY, Theesfeld CL, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet, 2019, 51(6): 973.
42. Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res, 2018, 24(6): 1248-1259.
43. Miotto R, Li L, Kidd BA, et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep-Uk, 2016, 6: 26094.
44. Liang H, Tsui BY, Ni H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med, 2019, 25(3): 433.
45. Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science, 1959, 130(3366): 9-21.
46. Warner HR, Toronto AF, Veasy LG. Experience with Baye's theorem for computer diagnosis of congenital heart disease. Ann N Y Acad Sci, 1964, 115(2): 558-567.
47. Bleich HL. Computer-based consultation: electrolyte and acid-base disorders. Am J Med, 1972, 53(3): 285-291.
48. Lederberg J. DENDRAL-64: A system for computer construction, enumeration and notation of organic molecules as tree structures and cyclic graphs, Part Ⅱ: Topology of cyclic graphs. 1965.
49. Shortliffe EH. MYCIN: a rule-based computer program for advising physicians regarding antimicrobial therapy selection. Stanf Univ Calif Dept of Comput Sci, 1974.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Artificial intelligence in congenital cardiology

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