Korean J Physiol Pharmacol.  2019 Sep;23(5):305-310. 10.4196/kjpp.2019.23.5.305.

Toward a grey box approach for cardiovascular physiome

• Hwang M
• Leem CH
• Shim EB

• KMID: 2455806
• DOI: http://doi.org/10.4196/kjpp.2019.23.5.305

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.

Keyword

Machine learning; Mathematical model; Patient-specific modeling

MeSH Terms

Cardiovascular Diseases
Diagnosis
Humans
Jurisprudence
Machine Learning
Models, Theoretical
Patient-Specific Modeling*

Figure

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  Fig. 1 Schematic machine learning (ML) algorithm. A set of the ML algorithms utilizing artificial neural network consisting of artificial neurons mimicking those of human (A), and convolutional neural network mainly used in image analysis (B).

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  Fig. 2 An example of image segmentation method coupling machine learning (ML) and mathematical segmentation algorithm. Here, there-dimensional model of coronary arteries was extracted from computed tomographic images.

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  Fig. 3 Comparison of the fractional flow reserve (FFR) contours of coronary arteries. (A) is computational fluid dynamics-based computed FFR distributions, and machine learning-based FFR distribution is shown in (B) (Figure from Itu L, et al. J Appl Physiol 2016;121:42-52 with permission [17]).

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  Fig. 4 Comparison of electric potential contours in two-dimensional cardiac tissue. Upper panel is computed from a mathematical model. Lower panel was obtained from machine learning algorithm. Colors in the images represent electric potential level of cardiac tissue.

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  Fig. 5 A machine leanring model for testing the effect of drugs on the QT interval. A surrogate model was built for the QT interval using a Gaussian process regression combining information from the mathematical modeling (Figure from Costabal FS, et al. Comput Methods Appl Mech Eng 2019;348:313–333 [22] [https://doi.org/10.1016/j.cma.2019.01.033] under the Creative Commons Attribution License [https://creativecommons.org/licenses/by/4.0/]).

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  Fig. 6 Schematic of grey box approach. Grey box approach combines white box and black box approaches and only retains the strengths of each approach. AI, artificial intelligence.

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