The Korean Journal of Physiology and Pharmacology

The Korean Society of Pharmacology (대한약리학회)
권호 표지
DOI QR코드

DOI QR Code

Toward a grey box approach for cardiovascular physiome

  • Received : 2019.07.10
  • Accepted : 2019.08.06
  • Published : 2019.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

Keywords

References

  1. Russell SJ, Norvig P. Artificial intelligence: a modern approach. 2nd ed. New Delhi: Prentice Hall; 2003.
  2. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
  3. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117. https://doi.org/10.1016/j.neunet.2014.09.003
  4. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. NIPS'12 Proceedings of the 25th International Conference on neural information processing systems. New York: Curran Associates Inc; 2012. p.1097-1105.
  5. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. NIPS'14 Proceedings of the 27th International Conference on neural information processing systems. Cambridge: MIT Press Cambridge; 2014. p.2672-2680.
  6. Ghahramani Z. Unsupervised learning. In: Bousquet O, von Luxburg U, Ratsch G, editors. Advanced lectures on machine learning. Berlin: New York :Springer; 2004.
  7. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102-127. https://doi.org/10.1016/j.zemedi.2018.11.002
  8. Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2017;6:9375-9389. https://doi.org/10.1109/access.2017.2788044
  9. Liu S, Wang Y, Yang X, Lei B, Liu L, Li S, Ni D, Wang T. Deep learning in medical ultrasound analysis: a review. Engineering. 2019;5:261-275. https://doi.org/10.1016/j.eng.2018.11.020
  10. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442
  11. Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Med Phys. 2019;29:86-101. https://doi.org/10.1016/j.zemedi.2018.12.003
  12. Bratt A, Kim J, Pollie M, Beecy AN, Tehrani NH, Codella N, Perez-Johnston R, Palumbo MC, Alakbarli J, Colizza W, Drexler IR, Azevedo CF, Kim RJ, Devereux RB, Weinsaft JW. Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification. J Cardiovasc Magn Reson. 2019;21:1. https://doi.org/10.1186/s12968-018-0509-0
  13. Kurata A, Fukuyama N, Hirai K, Kawaguchi N, Tanabe Y, Okayama H, Shigemi S, Watanabe K, Uetani T, Ikeda S, Inaba S, Kido T, Itoh T, Mochizuki T. On-site computed tomography-derived fractional flow reserve using a machine-learning algorithm- clinical effectiveness in a retrospective multicenter cohort. Circ J. 2019;83:1563-1571. https://doi.org/10.1253/circj.CJ-19-0163
  14. Wang Z. Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI. Hum Brain Mapp. 2014;35:2869-2875. https://doi.org/10.1002/hbm.22445
  15. Jordanski M, Radovic M, Milosevic Z, Filipovic N, Obradovic Z. Machine learning approach for predicting wall shear distribution for abdominal aortic aneurysm and carotid bifurcation models. IEEE J Biomed Health Inform. 2018;22:537-544. https://doi.org/10.1109/JBHI.2016.2639818
  16. Riordon J, Sovilj D, Sanner S, Sinton D, Young EWK. Deep learning with microfluidics for biotechnology. Trends Biotechnol. 2019;37:310-324. https://doi.org/10.1016/j.tibtech.2018.08.005
  17. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, Flohr T, Sharma P, Comaniciu D. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985). 2016;121:42-52. https://doi.org/10.1152/japplphysiol.00752.2015
  18. McKinley R, Hung F, Wiest R, Liebeskind DS, Scalzo F. A machine learning approach to perfusion imaging with dynamic susceptibility contrast MR. Front Neurol. 2018;9:717. https://doi.org/10.3389/fneur.2018.00717
  19. Cantwell CD, Mohamied Y, Tzortzis KN, Garasto S, Houston C, Chowdhury RA, Ng FS, Bharath AA, Peters NS. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling. Comput Biol Med. 2019;104:339-351. https://doi.org/10.1016/j.compbiomed.2018.10.015
  20. Lyon A, Minchole A, Martinez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface. 2018;15:20170821. https://doi.org/10.1098/rsif.2017.0821
  21. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463-477. https://doi.org/10.1038/s41573-019-0024-5
  22. Costabal FS, Matsuno K, Yao J, Perdikaris P, Kuhl E. Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Comput Methods Appl Mech Eng. 2019;348:313-333. https://doi.org/10.1016/j.cma.2019.01.033
  23. Deist TM, Patti A, Wang Z, Krane D, Sorenson T, Craft D. Simulation assisted machine learning. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz199. [Epub ahead of print]