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Deep Learning and Artificial Intelligence: What Does the Cardiologist Really Need to Know?

需要知道 人工智能 心理学 计算机科学 计算机安全
作者
James A. Case
出处
期刊:Circulation-cardiovascular Imaging [Lippincott Williams & Wilkins]
卷期号:15 (9)
标识
DOI:10.1161/circimaging.122.014744
摘要

HomeCirculation: Cardiovascular ImagingVol. 15, No. 9Deep Learning and Artificial Intelligence: What Does the Cardiologist Really Need to Know? No AccessEditorialRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessEditorialRequest AccessFull TextDeep Learning and Artificial Intelligence: What Does the Cardiologist Really Need to Know? James A. Case, PhD James A. CaseJames A. Case Correspondence to: James A. Case, PhD, MASNC Cardiovascular Imaging Technologies, University of Missouri Kansas City, Kansas City, MO. Email E-mail Address: [email protected] https://orcid.org/0000-0001-8050-2964 Cardiovascular Imaging Technologies, University of Missouri Kansas City. Search for more papers by this author Originally published20 Sep 2022https://doi.org/10.1161/CIRCIMAGING.122.014744Circulation: Cardiovascular Imaging. 2022;15This article is a commentary on the followingDeep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography ImagesFootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.This manuscript was sent to Gary Heller, MD, PhD, Guest Editor, for review by expert referees, editorial decision, and final disposition.For Disclosures, see page 660.Correspondence to: James A. Case, PhD, MASNC Cardiovascular Imaging Technologies, University of Missouri Kansas City, Kansas City, MO. Email [email protected]comReferences1. Turing AM. I.—Computing machinery and intelligence.Mind. 1950; LIX:433–460. doi: 10.1093/mind/LIX.236.433CrossrefGoogle Scholar2. Nilsson NJ. The Quest for Artificial Intelligence: a History of Ideas and Achievements. 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Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images.Circ Cardiovasc Imaging. 2022; 15:e014526. doi: 10.1161/CIRCIMAGING.122.014526LinkGoogle Scholar8. Moore G. Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff.Solid-State Circuits Newsl IEEE. 2006; 11:33–35. doi: 10.1109/N-SSC.2006.4785860CrossrefGoogle Scholar9. Garcia E, Maddahi J, Berman D, Waxman A. Space/time quantitation of thallium-201 myocardial scintigraphy.J Nucl Med. 1981; 22:309–317.MedlineGoogle Scholar10. Ficaro EP, Lee BC, Kritzman JN, Corbett JR. Corridor4DM: the Michigan method for quantitative nuclear cardiology.J Nucl Cardiol. 2007; 14:455–465. doi: 10.1016/j.nuclcard.2007.06.006CrossrefMedlineGoogle Scholar11. Berman DS, Kang X, Gransar H, Gerlach J, Friedman JD, Hayes SW, Thomson LE, Hachamovitch R, Shaw LJ, Slomka PJ, et al. Quantitative assessment of myocardial perfusion abnormality on SPECT myocardial perfusion imaging is more reproducible than expert visual analysis.J Nucl Cardiol. 2009; 16:45–53. doi: 10.1007/s12350-008-9018-0CrossrefMedlineGoogle Scholar12. Garcia EV, Cooke CD, Folks RD, Santana CA, Krawczynska EG, De Braal L, Ezquerra NF. Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies.J Nucl Med. 2001; 42:1185–1191.MedlineGoogle Scholar13. Slomka P, Betancur J, Otaki Y, Commandeur F, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, et al. Predicting major adverse cardiac events with cox neural networks: results from the refine spect registry.J Am Coll Cardiol. 2019; 73:1432–1432. doi:10.1016/s0735-1097(19)32038-8CrossrefGoogle Scholar14. Shrestha S, Sengupta PP. Machine learning for nuclear cardiology: the way forward.J Nucl Cardiol. 2019; 26:1755–1758. doi: 10.1007/s12350-018-1284-xCrossrefMedlineGoogle Scholar15. Gomez J, Doukky R. Artificial intelligence in nuclear cardiology.J Nucl Med. 2019; 60:1042–1043. doi: 10.2967/jnumed.118.222356CrossrefMedlineGoogle Scholar16. Santosh KC, Swarnendu Ghosh ND. Deep Learning Models for Medical Imaging. 1st ed. Academic Press; 2021.Google Scholar17. Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs.Radiology. 2019; 290:218–228. doi: 10.1148/radiol.2018180237CrossrefMedlineGoogle Scholar18. LeCun Y, Bengio Y, Hinton G. Deep learning.Nature. 2015; 521:436–444. doi: 10.1038/nature14539CrossrefMedlineGoogle Scholar19. Juarez-Orozco LE, Klén R, Niemi M, Ruijsink B, Daquarti G, van Es R, Benjamins JW, Yeung MW, van der Harst P, Knuuti J. Artificial intelligence to improve risk prediction with nuclear cardiac studies.Curr Cardiol Rep. 2022; 24:307–316. doi: 10.1007/s11886-022-01649-wCrossrefMedlineGoogle Scholar20. Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.Eur Heart J. 2017; 38:500–507. doi: 10.1093/eurheartj/ehw188CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesDeep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography ImagesPiotr J. Slomka, et al. Circulation: Cardiovascular Imaging. 2022;15 September 2022Vol 15, Issue 9 Advertisement Article InformationMetrics © 2022 American Heart Association, Inc.https://doi.org/10.1161/CIRCIMAGING.122.014744PMID: 36126127 Originally publishedSeptember 20, 2022 Keywordsmortalitydeep learningcomputersartificial intelligenceEditorialscardiologyPDF download Advertisement SubjectsNuclear Cardiology and PETPrognosis

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