Skip to main content
main-content
Erschienen in: Die Kardiologie 6/2021

02.11.2021 | Computertomografie | CME

Künstliche Intelligenz in der kardialen Computertomographie

verfasst von: Verena Brandt, MHBA FSCCT FESC PD Dr. med. Christian Tesche

Erschienen in: Die Kardiologie | Ausgabe 6/2021

zum CME-Kurs Einloggen, um Zugang zu erhalten

Zusammenfassung

Die kardiale Computertomographie (CT) ermöglicht neben einer präzisen Quantifizierung des Koronarkalks zur Risikostratifizierung die nichtinvasive anatomische sowie funktionelle Beurteilung von Koronarstenosen und Plaquemorphologie und stellt somit ein in den heutigen Leitlinien zur Diagnostik der koronaren Herzerkrankung (KHK) etabliertes Verfahren dar. Längst ist künstliche Intelligenz (KI) Teil unseres Lebens – und doch stehen wir am Beginn einer neuen Epoche in der Herzbildgebung. Die Fortschritte in der Entwicklung der KI und die Anwendung auf dem Gebiet der kardialen CT bieten neben vielen Möglichkeiten der Bildverbesserung und -optimierung eine höhere diagnostische Genauigkeit der anatomischen und funktionellen Beurteilung der KHK. KI-Verfahren sind lernende Systeme, welche mittels komplexer Algorithmen, wie dem maschinellen Lernen (ML) zur automatisierten Detektion und Analyse relevanter Bilddatenmerkmale, eingesetzt werden und eine Charakterisierung von Behandlungs- und Krankheitsverläufen sowie die Risikostratifizierung ermöglichen. Die Anwendung von KI-Methoden stellt zentrale Anforderungen an Wissenschaftler und Kliniker, deren Berücksichtigung für Ergebnisse hoher Präzision unabdingbar ist. Vielversprechende Ergebnisse von KI-Verfahren in der kardialen CT ermöglichen eine stetig wachsende Zahl von klinischen Anwendungen und die Entwicklung zu einem unverzichtbaren diagnostischen Tool in der modernen Herzbildgebung.
Literatur
1.
Zurück zum Zitat Chow BJ, Small G, Yam Y, CONFIRM Investigators et al (2011) Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRM: COroNary computed tomography angiography evaluation for clinical outcomes: an InteRnational multicenter registry. Circ Cardiovasc Imaging 4:463–472 PubMed Chow BJ, Small G, Yam Y, CONFIRM Investigators et al (2011) Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRM: COroNary computed tomography angiography evaluation for clinical outcomes: an InteRnational multicenter registry. Circ Cardiovasc Imaging 4:463–472 PubMed
2.
Zurück zum Zitat Maroules CD, Rajiah P, Bhasin M et al (2019) Current evidence in cardiothoracic imaging: growing evidence for coronary computed tomography angiography as a first-line test in stable chest pain. J Thorac Imaging 34:4–11 PubMed Maroules CD, Rajiah P, Bhasin M et al (2019) Current evidence in cardiothoracic imaging: growing evidence for coronary computed tomography angiography as a first-line test in stable chest pain. J Thorac Imaging 34:4–11 PubMed
3.
Zurück zum Zitat Meijboom WB, Van Mieghem CA, van Pelt N et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643 PubMed Meijboom WB, Van Mieghem CA, van Pelt N et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643 PubMed
4.
Zurück zum Zitat Gonzalez JA, Lipinski MJ, Flors L, Shaw PW, Kramer CM, Salerno M (2015) Meta-analysis of diagnostic performance of coronary computed tomography angiography, computed tomography perfusion, and computed tomography-fractional flow reserve in functional myocardial ischemia assessment versus invasive fractional flow reserve. Am J Cardiol 116(9):1469–1478 PubMedPubMedCentral Gonzalez JA, Lipinski MJ, Flors L, Shaw PW, Kramer CM, Salerno M (2015) Meta-analysis of diagnostic performance of coronary computed tomography angiography, computed tomography perfusion, and computed tomography-fractional flow reserve in functional myocardial ischemia assessment versus invasive fractional flow reserve. Am J Cardiol 116(9):1469–1478 PubMedPubMedCentral
5.
Zurück zum Zitat Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC guidelines forthe diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477 PubMed Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC guidelines forthe diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477 PubMed
6.
Zurück zum Zitat Al-Mallah MH, Aljizeeri A, Villines TC, Srichai MB, Alsaileek A (2015) Cardiac computed tomography in current cardiology guidelines. J Cardiovasc Comput Tomogr 9(6):514–523 PubMed Al-Mallah MH, Aljizeeri A, Villines TC, Srichai MB, Alsaileek A (2015) Cardiac computed tomography in current cardiology guidelines. J Cardiovasc Comput Tomogr 9(6):514–523 PubMed
7.
Zurück zum Zitat Maron DJ, Hochman JS, Reynolds HR et al (2020) Initial invasive or conservative strategy for stable coronary disease. N Engl J Med 382(15):1395–1407 PubMedPubMedCentral Maron DJ, Hochman JS, Reynolds HR et al (2020) Initial invasive or conservative strategy for stable coronary disease. N Engl J Med 382(15):1395–1407 PubMedPubMedCentral
8.
Zurück zum Zitat Grace K, Salvatier J, Dafoe A et al (2018) Viewpoint: when will aI exceed human performance? Evidence from aI experts. J Artif Intell Res 62:729–754 Grace K, Salvatier J, Dafoe A et al (2018) Viewpoint: when will aI exceed human performance? Evidence from aI experts. J Artif Intell Res 62:729–754
9.
Zurück zum Zitat Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH et al (2016) 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 38(7):500–507 PubMedCentral Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH et al (2016) 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 38(7):500–507 PubMedCentral
10.
Zurück zum Zitat Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416 Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
11.
Zurück zum Zitat Kleesiek J, Murray JM, Strack C et al (2020) Wie funktioniert maschinelles Lernen? Radiologe 60:24–31 PubMed Kleesiek J, Murray JM, Strack C et al (2020) Wie funktioniert maschinelles Lernen? Radiologe 60:24–31 PubMed
12.
Zurück zum Zitat Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117 PubMed Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117 PubMed
13.
Zurück zum Zitat Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349–360 Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349–360
14.
Zurück zum Zitat Breiman L (2001) Random forests. Mach Learn 45(1):5–32 Breiman L (2001) Random forests. Mach Learn 45(1):5–32
15.
Zurück zum Zitat Langs G, Attenberger U, Licandro R et al (2020) Maschinelles Lernen in der Radiologie. Radiologe 60:6–14 PubMed Langs G, Attenberger U, Licandro R et al (2020) Maschinelles Lernen in der Radiologie. Radiologe 60:6–14 PubMed
16.
Zurück zum Zitat van Assen M, Lee SJ, De Cecco CN (2020) Artificial intelligence from A to Z: from neural network to legal framework. Eur J Radiol 129:109083 PubMed van Assen M, Lee SJ, De Cecco CN (2020) Artificial intelligence from A to Z: from neural network to legal framework. Eur J Radiol 129:109083 PubMed
19.
Zurück zum Zitat Umehara K, Ota J, Ishida T (2018) Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging 31:441–450 PubMed Umehara K, Ota J, Ishida T (2018) Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging 31:441–450 PubMed
20.
Zurück zum Zitat Chaibi H, Nourine R (2018) New pseudo-CT generation approach from magnetic resonance imaging using a local texture descriptor. J Biomed Phys Eng 8(1):53–64 PubMedPubMedCentral Chaibi H, Nourine R (2018) New pseudo-CT generation approach from magnetic resonance imaging using a local texture descriptor. J Biomed Phys Eng 8(1):53–64 PubMedPubMedCentral
21.
Zurück zum Zitat McClelland RL, Chung H, Detrano R, Post W, Kronmal RA (2006) Distribution of coronary artery calcium by race, gender, and age: results from the multi-ethnic study of atherosclerosis (MESA). Circulation 113:30–37 PubMed McClelland RL, Chung H, Detrano R, Post W, Kronmal RA (2006) Distribution of coronary artery calcium by race, gender, and age: results from the multi-ethnic study of atherosclerosis (MESA). Circulation 113:30–37 PubMed
23.
Zurück zum Zitat Karoff M (2003) Herz-Kreislauf-Erkrankungen am Beispiel der koronaren Herzkrankheit und des akuten Myokardinfarkts. In: Schwartz FW, Badura B, Busse R, Leidl R, Raspe H, Siegrist J, Walter U (Hrsg) Das Public Health Buch. Gesundheit und Gesundheitswesen, 2. Aufl. Urban & Fischer, München, S 566–575 Karoff M (2003) Herz-Kreislauf-Erkrankungen am Beispiel der koronaren Herzkrankheit und des akuten Myokardinfarkts. In: Schwartz FW, Badura B, Busse R, Leidl R, Raspe H, Siegrist J, Walter U (Hrsg) Das Public Health Buch. Gesundheit und Gesundheitswesen, 2. Aufl. Urban & Fischer, München, S 566–575
24.
Zurück zum Zitat Erbel R, Mohlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, Dragano N, Gronemeyer D, Seibel R, Kalsch H, Brocker-Preuss M, Mann K, Siegrist J, Jockel KH, Heinz Nixdorf Recall Study Investigative Group (2010) Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf recall study. J Am Coll Cardiol 56:1397–1406 PubMed Erbel R, Mohlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, Dragano N, Gronemeyer D, Seibel R, Kalsch H, Brocker-Preuss M, Mann K, Siegrist J, Jockel KH, Heinz Nixdorf Recall Study Investigative Group (2010) Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf recall study. J Am Coll Cardiol 56:1397–1406 PubMed
25.
Zurück zum Zitat Victor RG, Haley RW, Willett DL, Peshock RM, Vaeth PC, Leonard D, Basit M, Cooper RS, Iannacchione VG, Visscher WA, Staab JM, Hobbs HH, Dallas Heart Study Investigators (2004) The Dallas heart study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am J Cardiol 93:1473–1480 PubMed Victor RG, Haley RW, Willett DL, Peshock RM, Vaeth PC, Leonard D, Basit M, Cooper RS, Iannacchione VG, Visscher WA, Staab JM, Hobbs HH, Dallas Heart Study Investigators (2004) The Dallas heart study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am J Cardiol 93:1473–1480 PubMed
26.
Zurück zum Zitat Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I (2019) Machine learning for assessment of coronary artery disease in cardiac CT: a survey. Front Cardiovasc Med 6:172 PubMedPubMedCentral Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I (2019) Machine learning for assessment of coronary artery disease in cardiac CT: a survey. Front Cardiovasc Med 6:172 PubMedPubMedCentral
27.
Zurück zum Zitat Wolterink JM, Leiner T, Takx RAP, Viergever MA, Išgum I (2015) Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection. IEEE Trans Med Imaging 34:1867–1878 PubMed Wolterink JM, Leiner T, Takx RAP, Viergever MA, Išgum I (2015) Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection. IEEE Trans Med Imaging 34:1867–1878 PubMed
28.
Zurück zum Zitat Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A et al (2020) Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. JACC Cardiovasc Imaging 13:524–526 PubMed Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A et al (2020) Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. JACC Cardiovasc Imaging 13:524–526 PubMed
29.
Zurück zum Zitat Van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard D, Leiner T et al (2020) Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology 295:66–79 PubMed Van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard D, Leiner T et al (2020) Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology 295:66–79 PubMed
30.
Zurück zum Zitat Yang G, Chen Y, Ning X, Sun Q, Shu H, Coatrieux JL (2016) Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys 43:2174 PubMed Yang G, Chen Y, Ning X, Sun Q, Shu H, Coatrieux JL (2016) Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys 43:2174 PubMed
31.
Zurück zum Zitat Cano-Espinosa C, González G, Washko GR, Cazorla M, Estépar RSJ (2018) Automated Agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng 10574:105742K PubMedPubMedCentral Cano-Espinosa C, González G, Washko GR, Cazorla M, Estépar RSJ (2018) Automated Agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng 10574:105742K PubMedPubMedCentral
32.
Zurück zum Zitat Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28(6):2655–2664 PubMedPubMedCentral Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28(6):2655–2664 PubMedPubMedCentral
33.
Zurück zum Zitat van Rosendael AR, Maliakal G, Kolli KK, Beecy A, Al’Aref SJ, Dwivedi A et al (2018) Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J Cardiovasc Comput Tomogr 12:204–209 PubMed van Rosendael AR, Maliakal G, Kolli KK, Beecy A, Al’Aref SJ, Dwivedi A et al (2018) Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J Cardiovasc Comput Tomogr 12:204–209 PubMed
34.
Zurück zum Zitat Johnson KM, Johnson HE, Zhao Y, Dowe DA, Staib LH (2019) Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology 292:354–362 PubMed Johnson KM, Johnson HE, Zhao Y, Dowe DA, Staib LH (2019) Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology 292:354–362 PubMed
35.
Zurück zum Zitat Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging 11:e7217 PubMed Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging 11:e7217 PubMed
36.
Zurück zum Zitat Tesche C, De Cecco CN, Baumann S, Renker M, McLaurin TW, Duguay TM et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72 PubMed Tesche C, De Cecco CN, Baumann S, Renker M, McLaurin TW, Duguay TM et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72 PubMed
37.
Zurück zum Zitat Tesche C, Otani K, De Cecco CN et al (2020) Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry. JACC Cardiovasc Imaging 13(3):760–770 PubMed Tesche C, Otani K, De Cecco CN et al (2020) Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry. JACC Cardiovasc Imaging 13(3):760–770 PubMed
38.
Zurück zum Zitat Han D, Lee JH, Rizvi A, Gransar H, Baskaran L, Schulman-Marcus J et al (2018) Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach. J Nucl Cardiol 25:223–233 PubMed Han D, Lee JH, Rizvi A, Gransar H, Baskaran L, Schulman-Marcus J et al (2018) Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach. J Nucl Cardiol 25:223–233 PubMed
39.
Zurück zum Zitat Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK (2015) Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal 24:77–89 PubMedPubMedCentral Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK (2015) Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal 24:77–89 PubMedPubMedCentral
40.
Zurück zum Zitat Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H et al (2015) Structured learning algorithm for detection of nonob-structive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging 2:14003 Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H et al (2015) Structured learning algorithm for detection of nonob-structive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging 2:14003
41.
Zurück zum Zitat Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I (2019) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38:1588–1598 PubMed Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I (2019) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38:1588–1598 PubMed
42.
Zurück zum Zitat Denzinger F et al (2020) Deep learning algorithms for coronary artery plaque characterisation from CCTA scans. In: Tolxdorff T, Deserno T, Handels H, Maier A, Maier-Hein K, Palm C (Hrsg) Bildverarbeitung für die Medizin 2020. Springer, Wiesbaden Denzinger F et al (2020) Deep learning algorithms for coronary artery plaque characterisation from CCTA scans. In: Tolxdorff T, Deserno T, Handels H, Maier A, Maier-Hein K, Palm C (Hrsg) Bildverarbeitung für die Medizin 2020. Springer, Wiesbaden
44.
Zurück zum Zitat Zhuang B, Wang S, Zhao S et al (2020) Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 30:712–725 PubMed Zhuang B, Wang S, Zhao S et al (2020) Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 30:712–725 PubMed
45.
Zurück zum Zitat Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd (2018) Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review. J Thorac Imaging 33:88–96 PubMed Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd (2018) Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review. J Thorac Imaging 33:88–96 PubMed
46.
Zurück zum Zitat Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52 Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52
47.
Zurück zum Zitat Matsumura-Nakano Y, Kawaji T, Shiomi H et al (2019) Optimal cutoff value of fractional flow reserve derived from coronary computed tomography angiography for predicting hemodynamically significant coronary artery disease. Circ Cardiovasc Imaging 12(8):e8905 PubMed Matsumura-Nakano Y, Kawaji T, Shiomi H et al (2019) Optimal cutoff value of fractional flow reserve derived from coronary computed tomography angiography for predicting hemodynamically significant coronary artery disease. Circ Cardiovasc Imaging 12(8):e8905 PubMed
48.
Zurück zum Zitat Tesche C, Vliegenthart R, Duguay TM, De Cecco CN, Albrecht MH, De Santis D et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol 120:2121–2127 PubMed Tesche C, Vliegenthart R, Duguay TM, De Cecco CN, Albrecht MH, De Santis D et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol 120:2121–2127 PubMed
49.
Zurück zum Zitat van Assen M, Varga-Szemes A, Schoepf UJ, Duguay TM, Hud-son HT, Egorova S et al (2019) Automated plaque analysis for the prognostication of major adverse cardiac events. Eur J Radiol 116:76–83 PubMed van Assen M, Varga-Szemes A, Schoepf UJ, Duguay TM, Hud-son HT, Egorova S et al (2019) Automated plaque analysis for the prognostication of major adverse cardiac events. Eur J Radiol 116:76–83 PubMed
Metadaten
Titel
Künstliche Intelligenz in der kardialen Computertomographie
verfasst von
Verena Brandt
MHBA FSCCT FESC PD Dr. med. Christian Tesche
Publikationsdatum
02.11.2021
Verlag
Springer Medizin
Erschienen in
Die Kardiologie / Ausgabe 6/2021
Print ISSN: 2731-7129
Elektronische ISSN: 2731-7137
DOI
https://doi.org/10.1007/s12181-021-00511-7