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For: Xue H, Davies RH, Brown LAE, Knott KD, Kotecha T, Fontana M, Plein S, Moon JC, Kellman P. Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning. Radiol Artif Intell 2020;2:e200009. [PMID: 33330849 DOI: 10.1148/ryai.2020200009] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 4.7] [Reference Citation Analysis]
Number Citing Articles
1 Allegra A, Mirabile G, Tonacci A, Genovese S, Pioggia G, Gangemi S. Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis. IJMS 2023;24:5680. [DOI: 10.3390/ijms24065680] [Reference Citation Analysis]
2 Wamil M, Goncalves M, Rutherford A, Borlotti A, Pellikka PA. Multi-modality cardiac imaging in the management of diabetic heart disease. Front Cardiovasc Med 2022;9. [DOI: 10.3389/fcvm.2022.1043711] [Reference Citation Analysis]
3 Martini N, Meloni A, Positano V, Latta DD, Keilberg P, Pistoia L, Spasiano A, Casini T, Barone A, Massa A, Ripoli A, Cademartiri F. Fully Automated Regional Analysis of Myocardial T2* Values for Iron Quantification Using Deep Learning. Electronics 2022;11:2749. [DOI: 10.3390/electronics11172749] [Reference Citation Analysis]
4 Keall PJ, Brighi C, Glide-Hurst C, Liney G, Liu PZY, Lydiard S, Paganelli C, Pham T, Shan S, Tree AC, van der Heide UA, Waddington DEJ, Whelan B. Integrated MRI-guided radiotherapy - opportunities and challenges. Nat Rev Clin Oncol 2022. [PMID: 35440773 DOI: 10.1038/s41571-022-00631-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
5 Teuho J, Schultz J, Klén R, Knuuti J, Saraste A, Ono N, Kanaya S. Classification of ischemia from myocardial polar maps in 15O-H2O cardiac perfusion imaging using a convolutional neural network. Sci Rep 2022;12:2839. [PMID: 35181681 DOI: 10.1038/s41598-022-06604-x] [Reference Citation Analysis]
6 Xue H, Artico J, Davies RH, Adam R, Shetye A, Augusto JB, Bhuva A, Fröjdh F, Wong TC, Fukui M, Cavalcante JL, Treibel TA, Manisty C, Fontana M, Ugander M, Moon JC, Schelbert EB, Kellman P. Automated In-Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance. J Am Heart Assoc 2022;:e023849. [PMID: 35132872 DOI: 10.1161/JAHA.121.023849] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Marwick TH, Gimelli A, Plein S, Bax JJ, Charron P, Delgado V, Donal E, Lancellotti P, Levelt E, Maurovich-Horvat P, Neubauer S, Pontone G, Saraste A, Cosyns B, Edvardsen T, Popescu BA, Galderisi M, Derumeaux G, Bäck M, Bertrand PB, Dweck M, Keenan N, Magne J, Neglia D, Stankovic I; Reviewers: This document was reviewed by members of the 2020–2022 EACVI Scientific Documents Committee. Multimodality imaging approach to left ventricular dysfunction in diabetes: an expert consensus document from the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 2022;23:e62-84. [PMID: 34739054 DOI: 10.1093/ehjci/jeab220] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
8 Passerini T, Yang Y, Chitiboi T, Oshinski JN. Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence. Artificial Intelligence in Cardiothoracic Imaging 2022. [DOI: 10.1007/978-3-030-92087-6_35] [Reference Citation Analysis]
9 Thornton GD, Shetye A, Knight DS, Knott K, Artico J, Kurdi H, Yousef S, Antonakaki D, Razvi Y, Chacko L, Brown J, Patel R, Vimalesvaran K, Seraphim A, Davies R, Xue H, Kotecha T, Bell R, Manisty C, Cole GD, Moon JC, Kellman P, Fontana M, Treibel TA. Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction. Front Cardiovasc Med 2021;8:764599. [PMID: 34950713 DOI: 10.3389/fcvm.2021.764599] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021;14:1133-46. [PMID: 34915726 DOI: 10.1161/CIRCIMAGING.121.013025] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
11 Maragna R, Giacari CM, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rossi A, Rabbat M, Pontone G. Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management. Front Cardiovasc Med 2021;8:736223. [PMID: 34631834 DOI: 10.3389/fcvm.2021.736223] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
12 Xue H, Artico J, Fontana M, Moon JC, Davies RH, Kellman P. Landmark Detection in Cardiac MRI by Using a Convolutional Neural Network. Radiol Artif Intell 2021;3:e200197. [PMID: 34617022 DOI: 10.1148/ryai.2021200197] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
13 Jacobs M, Benovoy M, Chang LC, Corcoran D, Berry C, Arai AE, Hsu LY. Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance. IEEE Access 2021;9:52796-811. [PMID: 33996344 DOI: 10.1109/access.2021.3070320] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]