Introduction
Each year CVD accounted for 17.9 million deaths, worldwide [4].
There was a surge of ~25% in CVD mortalities from 2000
to 2016 and the majority (85%) were either due to ischemic heart
disease or stroke. Low-and-middle-income countries with sociodemographic
index <0.75 [5] reported higher CVD death rate [6,7].
Further, years of life lost (YLL) increased in CVD while global trends
in total YLL decreased in other diseases [8]. The all-age
disability-adjusted life-years (DALY) in CVD soared 6.5% while
age-standardized DALY declined 16.9% from 2005 to 2015 [9].
Furthermore, financial stress raised incident CVD risk and all-cause
mortality in destitute women and single-men [10,11]. The risk is
more pronounced in diabetes [12] and hypertension [13]. To
summarize, CVDs deteriorates the quality of life both health-wise and
financially; it should, therefore, be identified at an earlier stage and
treated [5].
Atherosclerosis, the process of narrowing arterial walls, is the
underlying reason for CVD [14,15]. Expressed as a thickening on
arterial walls, atherosclerosis leads to macrophage accumulation,
necrotic core formation, fibrous-cap, and plaque buildup [16].
Plaque accumulates, perhaps, as a result of an inflammatory response
owing to the progress in atherosclerosis [16]. Several complications
of atherosclerosis; for example, plaque rupture, stenoses, obstruction
to blood flow and embolism, lead to severe conditions in CVD [17].
Atherosclerosis appearing in early childhood and young are linked to CVD
and remain silent (asymptomatic) for several years [18–20].
However, early-stage detection can prevent disease progression by
suitable lifestyle modification and medical intervention [17].
Several studies have observed in the literature that arterial stiffness
(or stiffness) and carotid intima-media thickness (IMT) are indicators
of atherosclerosis from inception [21–23].
Arterial stiffness describe the rigidity of arterial walls (or loss of
elasticity); is age-dependent [21], and augment as atherosclerosis
progress [24–27]. Several pathological changes involved in the
development of atherosclerosis are instigated in stiffness and both
often coexist. Note that mature adults with depression had augmented IMT
and stiffness values [28]. Further, arterial stiffness leads to
elevated blood pressure [29–32] and in hypertensives, augmented
stiffness indicate atherosclerotic CVD [33]. Importantly, elevated
stiffness is associated with atherosclerotic cardiovascular events (CVE)
[34], stroke [23,35], diastolic heart failure [33],
hypertension [29,30,36], diabetes [37,38], obesity [39–41]
and renal disease [42,43].
Several indices, pulse pressure (PP), compliance (C), distensibility
(D), pulse wave velocity (PWV), Young’s modulus, augmented index (AIx),
β-stiffness and Cardio-ankle vascular index (CAVI) are applied for the
quantification of arterial stiffness. PP is the difference between
systolic and diastolic pressure. Compliance is often described as the
ratio of change in volume to change in pressure
(\(C=\frac{\text{ΔV}}{\text{ΔP}}\)), and distensibility is viewed as
the compliance per original volume (\(D=\frac{C}{V}\)) [44]. A
brief description of various stiffness indices used in clinical studies
is provided in Appendix A .
PWV, the most significant measure owing to its association with CVE, is
defined as the velocity of pressure, diameter or flow-velocity wave.
Often significant variation in elasticity and impedance is observed in
arteries from proximal to peripheral [45]. This variation leads to
amplification, reflection and change in wave shape along the vasculature
attributed to composition, distending pressure, and muscle-tone of the
arterial wall [46] [45][47] [48]. Further, the wave
shape is dependent on age group [44,49,50], ageing [51,52],
physical fitness [53,54], insulin produced [55,56], heart rate,
body demographics and gender [57–59]. In short, pulse wave is
indicative of hypertension [60], diabetes [56,61,62], aortic
disintegration and heart failure [57,63]. From this point of
perspective, the assessment of PWV is a promising approach for
predicting atherosclerotic CVD.
Assessment of PWV includes invasive and non-invasive techniques.
However, non-invasive techniques are popular owing to its clinical
application. Clinically, PWV is estimated as the ratio of the distance
between two points in distal arteries and time taken by the wave to
cover this distance [64]. Based on the methodology used,
non-invasive assessment techniques are either “two-site” (regional) or
“one-site” (local). The two-site method includes traditional
“foot-by-foot” method [64] while the one-site method requires
multiple variables for assessment. Studies observed that branchial-ankle
PWV (ba-PWV) and carotid-femoral PWV (cfPWV) are highly correlated with
significant methods used in the assessment of PWV [65]. Acceptance
of any particular method for clinical purposes was debated earlier
[46,65]. However, cfPWV is considered “gold standard” due to its
strong association with CVD [37,43] and recommendation by various
committees [66]. Several challenges owing to the unstandardised
nature of assessment are discussed in a later section. Further, 80% of
the measured value of cfPWV and 10 m/s cutoff value is accepted
clinically [67]. The one-site method includes image-based techniques
that involve localisation and segmentation of the lumen region.
Performance of these techniques depends on the appearance of the artery
in a frame, operator experience, and equipment variability. However,
machine learning algorithms proved efficient in localisation and
segmentation of lumen and intima-media complex (IMC).
Machine learning systems extract patterns from raw-data and perform
specific tasks. These systems are supervised or unsupervised based on
the availability of labelled data [68]. During training, data is
presented to the system and it produces an output vector based primarily
on input and weights. An objective function measures error between the
output vector and the target vector (desired output) [69]. The
system then updates the weights to minimize this error [69]. This
technique is repeated for small sets of data until the mean of the
objective function stops decreasing [69]. Further, test-data is used
to assess performance on a generalized data and validation is done for
optimization [69]. The inspiration behind the application of DL
framework is to obtain human-like perfectness in tasks like
segmentation, object identification and classification. The application
of DL in various types of applications, such as lumen characterisation,
tumour segmentation and cell detection, has been a motivation for its
application on the carotid artery [70].
Major deep learning architectures include the recurrent neural networks
(RNN) [69], convolutional neural network (CNN) [69], deep belief
networks (DBN) [70] and autoencoders [71,72]. RNN is a network
wherein, the output of one layer is connected to the next layer. Partial
outputs are applied as feedback along with the next input [69]. RNN
is applied in speech and handwriting recognition. The key disadvantage
is the problem of vanishing gradients observed in the backpropagation
algorithm. Likewise, CNN is applied in object identification and
classification. The hidden layer in CNN includes stacks of convolution
layers, non-linear activation layers and pooling layers [69]. Moving
from the input layer, the first few layers extract low-level features
(edges) and “deeper” layers extract high-level features specific to
the object [69]. This network is often trained using backward
propagation [69]. Further, DBNs are applied in image and video
recognition tasks. DBN utilizes unsupervised learning, based on the
restricted Boltzmann machine (RBM) [70]. RBM is a two-layered
network wherein the first (input) is called the visible layer and the
second is called the hidden layer. In DBN, several RBMs are stacked:
hidden layer of one RBM is connected to the visible layer of second RBM
and so on. Each RBM is independently trained using unsupervised learning
and backpropagation network is applied at the end layers [70].
Likewise, autoencoders also learn features from raw data in an
unsupervised manner. This method offers efficient dimensionality
reduction and denoising capability. Herein, unlabeled inputs are
represented (encoded) using the most important features and the original
image (input image) is reconstructed from the encoded image. Different
types of autoencoders in practice are convolutional autoencoder, deep
autoencoder and contractive encoder. The most important challenge faced
by DL-based systems is the limited dataset. However, success using
transfer learning methods have motivated research in using other
methods, such as reinforced learning and autoencoders. Even so, DL
architectures suffer due to overfitting and underfitting. Overfitting
occurs when DL architecture gets trained to features more than what is
required and underfitting is when too little data is provided, and the
system is not modelled accurately. There are several methods that are
effectively applied to surmount underfitting and overfitting.
This review summarizes state-of-the-art techniques used for the
assessment of arterial stiffness. The article is organized into five
main sections: Section 2. will begin by describing conventional
techniques of PWV assessment and Section 3. will provide an insight into
image-based techniques. Besides, Section 4. will investigate the
application of DL-based systems in clinical diagnosis and the last
section will discuss stiffness computation using DL-framework.