Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy

Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimens...

Full description

Saved in:

Bibliographic Details
Published in:	Circulation. Cardiovascular imaging Vol. 9; no. 6
Main Authors:	Sengupta, Partho P, Huang, Yen-Min, Bansal, Manish, Ashrafi, Ali, Fisher, Matt, Shameer, Khader, Gall, Walt, Dudley, Joel T
Format:	Journal Article
Language:	English
Published:	United States 01.06.2016
Subjects:	Aged Algorithms Area Under Curve Biopsy Cardiomyopathy, Restrictive - classification Cardiomyopathy, Restrictive - diagnostic imaging Cardiomyopathy, Restrictive - physiopathology Case-Control Studies Decision Support Techniques Diagnosis, Differential Echocardiography, Doppler - methods Feasibility Studies Female Humans Machine Learning Male Middle Aged Myocardium - pathology Pericarditis, Constrictive - classification Pericarditis, Constrictive - diagnostic imaging Pericarditis, Constrictive - physiopathology Pilot Projects Predictive Value of Tests Reproducibility of Results ROC Curve Stroke Volume Ventricular Function, Left precision medicine cardiovascular imaging big data cognitive tools machine learning phenomics speckle tracking echocardiography
ISSN:	1942-0080, 1942-0080
Online Access:	Get more information
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.
AbstractList	Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.BACKGROUNDAssociating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.METHODS AND RESULTSClinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.CONCLUSIONSThis study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.
Author	Sengupta, Partho P Huang, Yen-Min Shameer, Khader Bansal, Manish Fisher, Matt Dudley, Joel T Gall, Walt Ashrafi, Ali
Author_xml	– sequence: 1 givenname: Partho P surname: Sengupta fullname: Sengupta, Partho P email: Partho.Sengupta@mountsinai.org organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.). Partho.Sengupta@mountsinai.org – sequence: 2 givenname: Yen-Min surname: Huang fullname: Huang, Yen-Min organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 3 givenname: Manish surname: Bansal fullname: Bansal, Manish organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 4 givenname: Ali surname: Ashrafi fullname: Ashrafi, Ali organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 5 givenname: Matt surname: Fisher fullname: Fisher, Matt organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 6 givenname: Khader surname: Shameer fullname: Shameer, Khader organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 7 givenname: Walt surname: Gall fullname: Gall, Walt organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.) – sequence: 8 givenname: Joel T surname: Dudley fullname: Dudley, Joel T organization: From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.)
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27266599$$D View this record in MEDLINE/PubMed
BookMark	eNpNkF1PwjAUhhuDkQ_9C6SX3gz7QbvNOzIFl6AS1GtSuhZqtha7YrIf4v91IEavzpvzPnlycvqgY51VAAwxGmHM8U2WL7P8cTLLn2btgo0QGlOKzkAPp2MSIZSgzr_cBf26fkeIU8SSC9AlMeGcpWkPfGVuY00wnwo-Crk1VkVzJbw1dgMn5cZ5E7YV1M7DTPjCCAnzSmza9hZO4MKULsCXsC-aI3JntFZe2WBEOAgyZ-vgjTzqF6pNB0cwNZx6V8Gl-muPdlc1bifCtrkE51qUtbo6zQF4m96_Zg_R_HmWZ5N5JCnnIRIqlgSnhFMqEk6UXKcxZQVRgmtZpJxipIXWCUpiyUQRa065Go-ZZGvJMFmTAbj-8e68-9i356wqU0tVlsIqt69XOE5ZwnEcoxYdntD9ulLFaudNJXyz-n0l-QZZWHzU
CitedBy_id	crossref_primary_10_1007_s10554_022_02566_3 crossref_primary_10_1097_RTI_0000000000000385 crossref_primary_10_1002_ehf2_12795 crossref_primary_10_1161_JAHA_119_013924 crossref_primary_10_1007_s10554_019_01545_5 crossref_primary_10_1016_j_nlm_2022_107623 crossref_primary_10_37615_retic_v2n1a1 crossref_primary_10_1016_j_heliyon_2022_e10872 crossref_primary_10_3389_fcvm_2021_711611 crossref_primary_10_1053_j_jvca_2020_08_048 crossref_primary_10_1007_s12170_023_00721_6 crossref_primary_10_1111_echo_15048 crossref_primary_10_1038_s41569_020_00477_1 crossref_primary_10_1093_eurheartj_ehy404 crossref_primary_10_3390_jimaging9020050 crossref_primary_10_1007_s11936_020_00838_6 crossref_primary_10_3390_diagnostics14020132 crossref_primary_10_1016_j_echo_2022_07_004 crossref_primary_10_1590_1806_9282_65_12_1438 crossref_primary_10_1016_j_cjca_2021_09_030 crossref_primary_10_1371_journal_pone_0201059 crossref_primary_10_3389_fcvm_2022_1016032 crossref_primary_10_1016_j_jcmg_2017_07_001 crossref_primary_10_1016_j_echo_2022_09_017 crossref_primary_10_1016_j_jacc_2017_03_571 crossref_primary_10_3390_jcdd11080245 crossref_primary_10_1016_j_cmpb_2019_105296 crossref_primary_10_3390_diagnostics10121004 crossref_primary_10_1038_s41569_021_00527_2 crossref_primary_10_1007_s11936_020_0805_5 crossref_primary_10_4274_atfm_galenos_2022_36449 crossref_primary_10_1016_j_amjcard_2020_08_048 crossref_primary_10_1016_j_tcm_2020_11_007 crossref_primary_10_1016_j_jcmg_2018_04_026 crossref_primary_10_1007_s11886_018_0990_y crossref_primary_10_1016_j_addr_2017_10_007 crossref_primary_10_1016_j_jacc_2016_12_047 crossref_primary_10_1016_j_ultrasmedbio_2022_06_004 crossref_primary_10_1007_s11886_022_01776_4 crossref_primary_10_1186_s12947_021_00261_2 crossref_primary_10_1016_j_rec_2019_05_014 crossref_primary_10_1007_s11936_019_0728_1 crossref_primary_10_1161_CIRCIMAGING_116_005059 crossref_primary_10_1016_j_jacc_2018_02_041 crossref_primary_10_3390_jpm13081268 crossref_primary_10_1136_openhrt_2019_001177 crossref_primary_10_1016_j_jacc_2017_01_062 crossref_primary_10_1111_echo_70007 crossref_primary_10_1016_j_pcad_2020_03_003 crossref_primary_10_1097_RTI_0000000000000584 crossref_primary_10_1007_s10278_024_01119_5 crossref_primary_10_1007_s13665_023_00334_9 crossref_primary_10_1002_jmri_28527 crossref_primary_10_3390_biomedicines10092157 crossref_primary_10_1016_j_bios_2021_113605 crossref_primary_10_1186_s12911_018_0653_3 crossref_primary_10_1016_j_cmpb_2022_107182 crossref_primary_10_1016_j_echo_2018_07_013 crossref_primary_10_1016_j_jacadv_2023_100574 crossref_primary_10_1515_bams_2020_0069 crossref_primary_10_1016_j_ultrasmedbio_2020_12_014 crossref_primary_10_1016_j_jacc_2016_08_062 crossref_primary_10_1007_s10554_020_01981_8 crossref_primary_10_3389_fcvm_2021_648877 crossref_primary_10_1007_s12596_018_0508_4 crossref_primary_10_1177_1179546820927404 crossref_primary_10_1016_j_tcm_2021_02_002 crossref_primary_10_1007_s12410_020_9529_x crossref_primary_10_1161_CIR_0000000000001202 crossref_primary_10_3389_fcvm_2019_00195 crossref_primary_10_1038_s41746_017_0013_1 crossref_primary_10_1016_j_imu_2024_101475 crossref_primary_10_1017_S1047951120001493 crossref_primary_10_3390_app9142921 crossref_primary_10_1016_j_jcmg_2023_09_011 crossref_primary_10_1007_s11886_020_01329_7 crossref_primary_10_36660_abc_20200596 crossref_primary_10_1002_ima_23086 crossref_primary_10_3389_fcvm_2023_1195235 crossref_primary_10_3390_life13041029 crossref_primary_10_1080_17434440_2017_1300057 crossref_primary_10_1097_HCO_0000000000000812 crossref_primary_10_1136_heartjnl_2017_311198 crossref_primary_10_4103_jcecho_jcecho_62_25 crossref_primary_10_1016_j_cpcardiol_2023_102349 crossref_primary_10_1016_j_pcad_2016_12_008 crossref_primary_10_1111_echo_14086 crossref_primary_10_1186_s13019_022_02025_z crossref_primary_10_1117_1_JMI_10_5_054502 crossref_primary_10_3389_fmed_2019_00036 crossref_primary_10_1016_j_echo_2018_10_003 crossref_primary_10_1161_CIRCRESAHA_116_309128 crossref_primary_10_1016_j_mayocp_2020_01_038 crossref_primary_10_1016_j_jcmg_2018_01_020 crossref_primary_10_3390_jcm14020625 crossref_primary_10_1007_s10554_020_02083_1 crossref_primary_10_1007_s11596_021_2486_z crossref_primary_10_1007_s12471_019_01311_1 crossref_primary_10_3389_fcvm_2022_945726 crossref_primary_10_1007_s11886_017_0851_0 crossref_primary_10_1007_s11886_022_01774_6 crossref_primary_10_1016_j_jcmg_2016_10_012 crossref_primary_10_2196_14784 crossref_primary_10_3390_diagnostics14020156 crossref_primary_10_1016_j_jcmg_2023_10_009 crossref_primary_10_3390_jcm11102893 crossref_primary_10_1371_journal_pone_0224453 crossref_primary_10_1007_s40471_019_00209_1 crossref_primary_10_1161_CIRCULATIONAHA_118_037095
ContentType	Journal Article
Copyright	2016 American Heart Association, Inc.
Copyright_xml	– notice: 2016 American Heart Association, Inc.
DBID	CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1161/CIRCIMAGING.115.004330
DatabaseName	Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	no_fulltext_linktorsrc
EISSN	1942-0080
ExternalDocumentID	27266599
Genre	Journal Article
GrantInformation_xml	– fundername: NCI NIH HHS grantid: U54 CA189201 – fundername: NCATS NIH HHS grantid: UL1 TR000067
GroupedDBID	--- .XZ .Z2 0R~ 18M 29B 53G 5GY 5VS 6J9 AAAAV AAHPQ AAIQE AAJCS AARTV AASCR ABASU ABBUW ABDIG ABJNI ABVCZ ABXVJ ABXYN ABZAD ABZZY ACDDN ACEWG ACGFO ACGFS ACILI ACWDW ACWRI ACXJB ACXNZ ADBBV ADGGA ADHPY ADNKB AEBDS AEETU AFBFQ AFDTB AFEXH AFNMH AFUWQ AGINI AHQNM AHQVU AHRYX AHVBC AINUH AJCLO AJIOK AJNWD AJNYG AJZMW AKCTQ ALKUP ALMA_UNASSIGNED_HOLDINGS ALMTX AMJPA AMKUR AMNEI AOHHW AOQMC BAWUL BQLVK C45 CGR CS3 CUY CVF DIK DIWNM DUNZO E.X E3Z EBS ECM EEVPB EIF EJD EX3 F5P FCALG FL- GNXGY GQDEL H13 HLJTE HZ~ IKREB IN~ IPNFZ KD2 KQ8 KQB L-C NPM O9- ODMTH ODZKP OHYEH OK1 OPUJH OUVQU OVD OVDNE OXXIT P2P P6G RAH RIG RLZ S4S TEORI TR2 TSPGW V2I W2D W3M W8F WOW ZZMQN 7X8 ABPXF ADKSD
ID	FETCH-LOGICAL-c366t-ae7c2192633a862ecb9735d2ea6fcd96310faff8087c5ad7f636e445c5bc512b2
IEDL.DBID	7X8
ISICitedReferencesCount	94
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000378135400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1942-0080
IngestDate	Mon Sep 08 06:26:07 EDT 2025 Thu Apr 03 07:00:08 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Keywords	precision medicine cardiovascular imaging big data cognitive tools machine learning phenomics speckle tracking echocardiography
Language	English
License	2016 American Heart Association, Inc.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c366t-ae7c2192633a862ecb9735d2ea6fcd96310faff8087c5ad7f636e445c5bc512b2
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PMID	27266599
PQID	1795861770
PQPubID	23479
ParticipantIDs	proquest_miscellaneous_1795861770 pubmed_primary_27266599
PublicationCentury	2000
PublicationDate	2016-Jun 20160601
PublicationDateYYYYMMDD	2016-06-01
PublicationDate_xml	– month: 06 year: 2016 text: 2016-Jun
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Circulation. Cardiovascular imaging
PublicationTitleAlternate	Circ Cardiovasc Imaging
PublicationYear	2016
References	3203132 - Biometrics. 1988 Sep;44(3):837-45 12804087 - J Comput Biol. 2003;10(2):119-42 18550044 - Comput Biol Med. 2008 Jul;38(7):817-25 21957173 - Sci Transl Med. 2011 Sep 28;3(102):102ra95 19130999 - J Am Soc Echocardiogr. 2009 Jan;22(1):24-33; quiz 103-4 27266601 - Circ Cardiovasc Imaging. 2016 Jun;9(6) 19187853 - J Am Soc Echocardiogr. 2009 Feb;22(2):107-33 26073787 - Med Image Anal. 2015 Aug;24(1):77-89 26141982 - J Am Soc Echocardiogr. 2015 Oct;28(10 ):1204-1213, e2 21338865 - J Am Soc Echocardiogr. 2011 Mar;24(3):277-313 10454978 - Eur Heart J. 1999 Sep;20(17):1271-5 18638602 - Am J Cardiol. 2008 Aug 1;102(3):357-62 23532508 - Circ Cardiovasc Imaging. 2013 May 1;6(3):399-406 24633783 - Circ Cardiovasc Imaging. 2014 May;7(3):526-34 15905277 - Bioinformatics. 2005 Aug 1;21(15):3301-7 23286090 - Med Image Comput Comput Assist Interv. 2012;15(Pt 2):535-43 26209911 - J Am Soc Echocardiogr. 2015 Oct;28(10):1171-1181, e2 24229773 - JACC Cardiovasc Imaging. 2013 Nov;6(11):1206-11 20362924 - J Am Soc Echocardiogr. 2010 Apr;23 (4):351-69; quiz 453-5 25888476 - BMJ Open. 2015 Apr 17;5(4):e007170 19356402 - JACC Cardiovasc Imaging. 2008 Jan;1(1):29-38 25107553 - Circ Cardiovasc Imaging. 2014 Sep;7(5):819-27 19342117 - Comput Methods Programs Biomed. 2009 Aug;95(2 Suppl):S22-32 26403342 - J Am Coll Cardiol. 2015 Sep 29;66(13):1456-66 1993786 - J Am Coll Cardiol. 1991 Mar 1;17(3):666-9
References_xml	– reference: 23286090 - Med Image Comput Comput Assist Interv. 2012;15(Pt 2):535-43 – reference: 1993786 - J Am Coll Cardiol. 1991 Mar 1;17(3):666-9 – reference: 24633783 - Circ Cardiovasc Imaging. 2014 May;7(3):526-34 – reference: 18550044 - Comput Biol Med. 2008 Jul;38(7):817-25 – reference: 25107553 - Circ Cardiovasc Imaging. 2014 Sep;7(5):819-27 – reference: 19187853 - J Am Soc Echocardiogr. 2009 Feb;22(2):107-33 – reference: 26209911 - J Am Soc Echocardiogr. 2015 Oct;28(10):1171-1181, e2 – reference: 21957173 - Sci Transl Med. 2011 Sep 28;3(102):102ra95 – reference: 21338865 - J Am Soc Echocardiogr. 2011 Mar;24(3):277-313 – reference: 19130999 - J Am Soc Echocardiogr. 2009 Jan;22(1):24-33; quiz 103-4 – reference: 10454978 - Eur Heart J. 1999 Sep;20(17):1271-5 – reference: 19342117 - Comput Methods Programs Biomed. 2009 Aug;95(2 Suppl):S22-32 – reference: 25888476 - BMJ Open. 2015 Apr 17;5(4):e007170 – reference: 26073787 - Med Image Anal. 2015 Aug;24(1):77-89 – reference: 19356402 - JACC Cardiovasc Imaging. 2008 Jan;1(1):29-38 – reference: 24229773 - JACC Cardiovasc Imaging. 2013 Nov;6(11):1206-11 – reference: 18638602 - Am J Cardiol. 2008 Aug 1;102(3):357-62 – reference: 26141982 - J Am Soc Echocardiogr. 2015 Oct;28(10 ):1204-1213, e2 – reference: 26403342 - J Am Coll Cardiol. 2015 Sep 29;66(13):1456-66 – reference: 20362924 - J Am Soc Echocardiogr. 2010 Apr;23 (4):351-69; quiz 453-5 – reference: 12804087 - J Comput Biol. 2003;10(2):119-42 – reference: 23532508 - Circ Cardiovasc Imaging. 2013 May 1;6(3):399-406 – reference: 27266601 - Circ Cardiovasc Imaging. 2016 Jun;9(6): – reference: 3203132 - Biometrics. 1988 Sep;44(3):837-45 – reference: 15905277 - Bioinformatics. 2005 Aug 1;21(15):3301-7
SSID	ssj0063058
Score	2.5317407
Snippet	Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical...
SourceID	proquest pubmed
SourceType	Aggregation Database Index Database
SubjectTerms	Aged Algorithms Area Under Curve Biopsy Cardiomyopathy, Restrictive - classification Cardiomyopathy, Restrictive - diagnostic imaging Cardiomyopathy, Restrictive - physiopathology Case-Control Studies Decision Support Techniques Diagnosis, Differential Echocardiography, Doppler - methods Feasibility Studies Female Humans Machine Learning Male Middle Aged Myocardium - pathology Pericarditis, Constrictive - classification Pericarditis, Constrictive - diagnostic imaging Pericarditis, Constrictive - physiopathology Pilot Projects Predictive Value of Tests Reproducibility of Results ROC Curve Stroke Volume Ventricular Function, Left
Title	Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy
URI	https://www.ncbi.nlm.nih.gov/pubmed/27266599 https://www.proquest.com/docview/1795861770
Volume	9
WOSCitedRecordID	wos000378135400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText
inHoldings	1
isFullTextHit
isPrint
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA7qfPDFC97mjQi-hnVNk6y-yKhuDtwYQ2VvI03SOVjXuVVhP8T_60nWuSdB8KWUXg4h-ZJ8yTk5H0I3psoT4RlGQpheSMAkhbvEkDDWUivpK89pEbw-iU6n1u-H3WLDbV6EVa7GRDdQ60zZPfIKAIfVYLoV3t30nVjVKOtdLSQ0NlGJApWxIV2i_-NF4IBldxQuDHxiqVFxQhhITiVq9aJWu95sdZrwwO6rBJR6v9NMN9009v5b0H20WxBNXF8i4wBtmMkh-opW0UK47aIoDSkSrA5xfTwEM_lbioHH4sghR-FW6mSMbnEdd0fjLMc28HDhPrkvtFVy27pgwGp_2nz_znzXOYLARj6a48YsS3HPrN8661m6yKwg8uIIvTQenqNHUggzEEU5z4k0QsFI53NKJayIjIpDQZn2jeSJ0tClq14ik6Tm1YRiUouEU26CgCkWKyAYsX-MtibZxJwirEJf2lVMbDwdwNoFIAVEmksNtI5pE5TR9aqWBwB8682QE5N9zAfrei6jk2VTDabLDB0DXwDvYGF49oe_z9EOkCC-DP-6QKUEur25RNvqEypoduUQBddOt_0N0frZIQ
linkProvider	ProQuest
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Cognitive+Machine-Learning+Algorithm+for+Cardiac+Imaging%3A+A+Pilot+Study+for+Differentiating+Constrictive+Pericarditis+From+Restrictive+Cardiomyopathy&rft.jtitle=Circulation.+Cardiovascular+imaging&rft.au=Sengupta%2C+Partho+P&rft.au=Huang%2C+Yen-Min&rft.au=Bansal%2C+Manish&rft.au=Ashrafi%2C+Ali&rft.date=2016-06-01&rft.eissn=1942-0080&rft.volume=9&rft.issue=6&rft_id=info:doi/10.1161%2FCIRCIMAGING.115.004330&rft_id=info%3Apmid%2F27266599&rft_id=info%3Apmid%2F27266599&rft.externalDocID=27266599
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1942-0080&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1942-0080&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1942-0080&client=summon