Machine Learning in Cardiac CT

Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and pro...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Landreth, Scott P. [verfasserIn] Spearman, James V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Imaging Cardiac CT Cardiac computed tomography Machine learning

Übergeordnetes Werk:	Enthalten in: Current radiology reports - New York, NY : Springer, 2013, 5(2017), 10 vom: 03. Aug.
Übergeordnetes Werk:	volume:5 ; year:2017 ; number:10 ; day:03 ; month:08

Links:	Volltext

DOI / URN:	10.1007/s40134-017-0241-9

Katalog-ID:	SPR033530807

Internformat


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allfields	10.1007/s40134-017-0241-9 doi (DE-627)SPR033530807 (SPR)s40134-017-0241-9-e DE-627 ger DE-627 rakwb eng 610 ASE Landreth, Scott P. verfasserin aut Machine Learning in Cardiac CT 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging. Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Spearman, James V. verfasserin aut Enthalten in Current radiology reports New York, NY : Springer, 2013 5(2017), 10 vom: 03. Aug. (DE-627)739215027 (DE-600)2708002-X 2167-4825 nnns volume:5 year:2017 number:10 day:03 month:08 https://dx.doi.org/10.1007/s40134-017-0241-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2017 10 03 08
spelling	10.1007/s40134-017-0241-9 doi (DE-627)SPR033530807 (SPR)s40134-017-0241-9-e DE-627 ger DE-627 rakwb eng 610 ASE Landreth, Scott P. verfasserin aut Machine Learning in Cardiac CT 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging. Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Spearman, James V. verfasserin aut Enthalten in Current radiology reports New York, NY : Springer, 2013 5(2017), 10 vom: 03. Aug. (DE-627)739215027 (DE-600)2708002-X 2167-4825 nnns volume:5 year:2017 number:10 day:03 month:08 https://dx.doi.org/10.1007/s40134-017-0241-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2017 10 03 08
allfields_unstemmed	10.1007/s40134-017-0241-9 doi (DE-627)SPR033530807 (SPR)s40134-017-0241-9-e DE-627 ger DE-627 rakwb eng 610 ASE Landreth, Scott P. verfasserin aut Machine Learning in Cardiac CT 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging. Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Spearman, James V. verfasserin aut Enthalten in Current radiology reports New York, NY : Springer, 2013 5(2017), 10 vom: 03. Aug. (DE-627)739215027 (DE-600)2708002-X 2167-4825 nnns volume:5 year:2017 number:10 day:03 month:08 https://dx.doi.org/10.1007/s40134-017-0241-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2017 10 03 08
allfieldsGer	10.1007/s40134-017-0241-9 doi (DE-627)SPR033530807 (SPR)s40134-017-0241-9-e DE-627 ger DE-627 rakwb eng 610 ASE Landreth, Scott P. verfasserin aut Machine Learning in Cardiac CT 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging. Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Spearman, James V. verfasserin aut Enthalten in Current radiology reports New York, NY : Springer, 2013 5(2017), 10 vom: 03. Aug. (DE-627)739215027 (DE-600)2708002-X 2167-4825 nnns volume:5 year:2017 number:10 day:03 month:08 https://dx.doi.org/10.1007/s40134-017-0241-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2017 10 03 08
allfieldsSound	10.1007/s40134-017-0241-9 doi (DE-627)SPR033530807 (SPR)s40134-017-0241-9-e DE-627 ger DE-627 rakwb eng 610 ASE Landreth, Scott P. verfasserin aut Machine Learning in Cardiac CT 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging. Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Spearman, James V. verfasserin aut Enthalten in Current radiology reports New York, NY : Springer, 2013 5(2017), 10 vom: 03. Aug. (DE-627)739215027 (DE-600)2708002-X 2167-4825 nnns volume:5 year:2017 number:10 day:03 month:08 https://dx.doi.org/10.1007/s40134-017-0241-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2017 10 03 08
language	English
source	Enthalten in Current radiology reports 5(2017), 10 vom: 03. Aug. volume:5 year:2017 number:10 day:03 month:08
sourceStr	Enthalten in Current radiology reports 5(2017), 10 vom: 03. Aug. volume:5 year:2017 number:10 day:03 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Imaging Cardiac CT Cardiac computed tomography Machine learning
dewey-raw	610
isfreeaccess_bool	false
container_title	Current radiology reports
authorswithroles_txt_mv	Landreth, Scott P. @@aut@@ Spearman, James V. @@aut@@
publishDateDaySort_date	2017-08-03T00:00:00Z
hierarchy_top_id	739215027
dewey-sort	3610
id	SPR033530807
language_de	englisch
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spellingShingle	Landreth, Scott P. ddc 610 misc Imaging misc Cardiac CT misc Cardiac computed tomography misc Machine learning Machine Learning in Cardiac CT
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topic_title	610 ASE Machine Learning in Cardiac CT Imaging (dpeaa)DE-He213 Cardiac CT (dpeaa)DE-He213 Cardiac computed tomography (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213
topic	ddc 610 misc Imaging misc Cardiac CT misc Cardiac computed tomography misc Machine learning
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title	Machine Learning in Cardiac CT
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title_full	Machine Learning in Cardiac CT
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author_browse	Landreth, Scott P. Spearman, James V.
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title_sort	machine learning in cardiac ct
title_auth	Machine Learning in Cardiac CT
abstract	Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging.
abstractGer	Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging.
abstract_unstemmed	Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. Recent Findings This review covers recent publications for automated image processing, diagnostic and prognostic support, as well as novel integrations of ML into extant imaging applications in advanced cardiac computed tomography. Where available, ML algorithms are compared to current gold standards and descriptions of the nature and value of the advances are described. Summary Machine learning in clinical imaging is considered by many to represent one of the most promising areas of research and development in diagnostic radiology. Sophisticated Machine Learning systems like IBM’s Watson have captured the public’s attention in their ability to mimic human capacity for pattern recognition in extremely large data sets. There are numerous recent research publications utilizing Machine Learning algorithms to either automate processes or improve diagnosis or even create entirely new forms of evaluation previously considered out of reach for cardiac CT imaging.
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title_short	Machine Learning in Cardiac CT
url	https://dx.doi.org/10.1007/s40134-017-0241-9
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up_date	2024-07-03T18:54:21.028Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR033530807</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519092544.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40134-017-0241-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR033530807</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40134-017-0241-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Landreth, Scott P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning in Cardiac CT</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose of Review This review covers the basic principles of machine learning (ML) and current applications in the subspecialty of cardiac imaging at computed tomography in diagnostic radiology. 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