cf65656e-7362-4081-8113-3facb48719a8 beie director@blueeyesintelligence.org 10.35940/ijrte.F7480.0311623 World Health Organization. (2019). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)NHS website. (2022, June 24). Cardiovascular disease. nhs.uk. Retrieved from https://www.nhs.uk/conditions/cardiovascular-disease/10.1093/eurheartj/ehy404Al'Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, Pandey M, Maliakal G, van Rosendael AR, Beecy AN, Berman DS, Leipsic J, Nieman K, Andreini D, Pontone G, Schoepf UJ, Shaw LJ, Chang HJ, Narula J, Bax JJ, Guan Y, Min JK. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019 Jun 21;40(24):1975-1986. doi: 10.1093/eurheartj/ehy404. PMID: 30060039. [CrossRef]10.1097/MCC.0000000000000949Aaron D. Aguirre, Kenneth T. Shelton."Remote monitoring in the use of extracorporeal membrane oxygenation and acute mechanical circulatory support", Current Opinion in Critical Care, 2022 Publication Viswanathan Rajagopalan, Houwei Cao. [CrossRef]10.1109/ICICT50816.2021.9358733T P Pushpavathi, Santhosh Kumari, N K Kubra. "Heart Failure Prediction by Feature Ranking Analysis in Machine Learning", 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021 [CrossRef]Rahul Deo, Brigham and Women's Hospital, University of California San Francisco, 2019. https://ocw.mit.edu/courses/6-s897-machine-learning-for-healthcare-spring-2019/pages/lecture-notes/10.1109/TMI.2018.2837502O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al."Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?" in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018 doi: 10.1109/TMI.2018.2837502 [CrossRef]10.1007/s12350-018-1326-4Slomka PJ, Betancur J, Liang JX, Otaki Y, Hu LH, Sharir T, Dorbala S, Di Carli M, Fish MB, Ruddy TD, Bateman TM, Einstein AJ, Kaufmann PA, Miller EJ, Sinusas AJ, Azadani PN, Gransar H, Tamarappoo BK, Dey D, Berman DS, Germano G. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). J Nucl Cardiol. 2020 Jun;27(3):1010-1021. doi: 10.1007/s12350-018-1326-4. Epub 2018 Jun 19. PMID: 29923104; PMCID: PMC6301135. [CrossRef]The German National Cohort, Nako database, 2011. https://nako.de/10.24078/psy.2022.3.128670Onderzoekscentrum (2022, July 29). De Maastricht Studie. https://www.demaastrichtstudie.nl/ [CrossRef]Canadian Alliance for Healthy Hearts and Minds, 2013, https://cahhm.mcmaster.ca/Challenge on Endocardial Three-dimensional Ultrasound Segmentation, MICCAI challenge 2014. https://www.creatis.insa-lyon.fr/Challenge/CETUS/databases.htmlDie Hamburg City Health Study (HCHS), Hamburg City, November 2021, Available at : https://hchs.hamburg/Automated Cardiac Diagnosis Challenge (ACDC), 2017. https://www.creatis.insa-lyon.fr/Challenge/acdc/CAMUS, Cardiac Acquisitions for Multi-structure Ultrasound Segmentation, University Hospital of St Etienne (France), Available at : https://www.creatis.insa-lyon.fr/Challenge/camus/databases.html10.1109/TPAMI.2018.2869576Xiahai Zhuang: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(12): 2933-2946, 2019. [CrossRef]10.1016/j.media.2016.02.006Xiahai Zhuang and Juan Shen: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI, Medical Image Analysis 31: 77-87, 2016 [CrossRef]10.1109/TPAMI.2022.3225418X Luo & X Zhuang: X-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022-11 online published (IF: 24.314) [CrossRef]10.1038/nature14539Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015. [CrossRef]10.21037/qims-20-745Kim T, Hedayat M, Vaitkus VV, Belohlavek M, Krishnamurthy V, Borazjani I. Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks. Quant Imaging Med Surg. 2021 May;11(5):1763-1781. doi: 10.21037/qims-20-745. PMID: 33936963; PMCID: PMC8047352. [CrossRef]Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimed Tools Appl. 2021;80(16):24365-24398. doi: 10.1007/s11042-021-10707-4. Epub 2021 Apr 6. PMID: 33841033; PMCID: PMC8023554.10.3390/electronics11213594Bhan, A.; Mangipudi, P.; Goyal, A. Deep Learning Approach for Automatic Segmentation and Functional Assessment of LV in Cardiac MRI. Electronics 2022, 11, 3594. https://doi.org/10.3390/electronics11213594 [CrossRef]10.1101/2021.07.26.21261115Nick James, Lianna Gerrish, Nikita Rokotyan, Patrick A. Gladding. "Machine Learning Applied to Routine Blood Tests and Clinical Metadata to Identify and Classify Heart failure", Cold Spring Harbor Laboratory, 2021 [CrossRef]10.1016/j.echo.2018.07.013Tabassian M, Sunderji I, Erdei T, Sanchez-Martinez S, Degiovanni A, Marino P, et al. Diagnosis of heart failure with preserved ejection fraction: machine learning of spatiotemporal variations in left ventricular deformation. J Am Soc Echocardiogr. (2018) 31:1272-84. doi: 10.1016/j.echo.2018.07.013 [CrossRef]10.1007/978-3-319-75541-0_9Cetin I, Sanroma G, Petersen SE, Napel S, Camara O, Ballester MAG, et al. A radiomics approach to computer-aided diagnosis with cardiac cine-MRI. In: InternationalWorkshop on Statistical Atlases and ComputationalModels of the Heart. Quebec City, QC: Springer (2017). p. 82-90. [CrossRef]10.1109/ICCUBEA.2018.8697795Borkar S, Annadate M. Supervised machine learning algorithm for detection of cardiac disorders. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). Pune: IEEE (2018). p.1-4. [CrossRef]10.1016/j.compbiomed.2016.03.026Moghaddasi H, Nourian S, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med. (2016) 73:47-55. doi: 10.1016/j.compbiomed.2016.03.026 [CrossRef]10.1109/STSIVA.2019.8730231Moreno A, Rodriguez J, Martínez F. Regional multiscale motion representation for cardiac disease prediction. In: 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). Bucaramanga, CO: IEEE (2019). p. 1-5. [CrossRef]10.1016/j.ejrad.2018.03.013Baeßler B, Mannil M, Maintz D, Alkadhi H, Manka R. Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-preliminary results. Eur J Radiol. (2018) 102:61-7. doi: 10.1016/j.ejrad.2018.03.013 [CrossRef]10.1148/radiol.2017170213Baeßler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R. Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology. (2017) 286:103-12. doi: 10.1148/radiol.2017170213 [CrossRef]10.1007/978-3-319-75541-0_11Wolterink JM, Leiner T, Viergever MA, Išgum I. Automatic segmentation and disease classification using cardiac cine MR images. In: International Workshop on Statistical Atlases and Computational Models of the Heart. Quebec City, QC: Springer (2017). p. 101-10. [CrossRef]10.1109/ISBI.2018.8363858Lu A, Dehghan E, Veni G, Moradi M, Syeda-Mahmood T. Detecting anomalies from echocardiography using multi-view regression of clinical measurements. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018). p. 1504-8. [CrossRef]10.1016/j.media.2016.04.004Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. (2016) 34:123-36. doi: 10.1016/j.media.2016.04.004 [CrossRef]10.1109/ISBI.2019.8759276Snaauw G, Gong D, Maicas G, van den Hengel A, Niessen WJ, Verjans J, et al. End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Venice: IEEE (2019). p. 802-5. [CrossRef]10.1016/j.media.2018.10.004Khened, Mahendra, Kollerathu, Varghese Alex, Krishnamurthi, Ganapathy, et al. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers, Medical image analysis, 2019, Elsevier [CrossRef]10.1109/ISPCC53510.2021.9609408Ajay Sharma, Raj Kumar, Varun Jaiswal, et al. Classification of Heart Disease from MRI Images Using Convolutional Neural Network,IEEE International Conference on Signal Processing, Computing and Control 2021, JUIT, Solan, India [CrossRef]10.1007/978-3-319-75541-0_11Jelmer M. Wolterink(B), Tim Leiner, Max A. Viergever, Ivana Iˇsgum1, et al. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images, Springer International Publishing AG, part of Springer Nature 2018 [CrossRef]S. Brown, J. Lee, and Y. Kim, "Automated Segmentation of Myocardial Infarction using Convolutional Neural Networks," Computerized Medical Imaging and Graphics, vol. 58, pp. 1-9, 2022.10.1002/ejhf.1333Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. (2019) 21:74-85. doi: 10.1002/ejhf.1333 [CrossRef]10.1161/CIRCIMAGING.117.006843Kolossváry M, Karády J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, et al. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circulation. (2017) 10:e0 06843. doi: 10.1161/CIRCIMAGING.117.006843 [CrossRef]10.1148/radiol.2019182304Zhang N, Yang G, Gao Z, Xu C, Zhang Y, Shi R, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac Cine MRI. Radiology. (2019) 291:606-17. doi: 10.1148/radiol.2019 182304 [CrossRef]10.1007/s12350-017-0834-yHan D, Lee JH, Rizvi A, Gransar H, Baskaran L, Schulman-Marcus J, et al. Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach. J Nucl Cardiol. (2018) 25:223-33. doi: 10.1007/s12350-017-0834-y [CrossRef]