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Figure 2.8 Formation of plaques in the common carotid artery and internal carotid artery.

One of the ways to identify the plaques in the arterial wall is by analyzing the carotid artery, which consists of a pair of blood vessels and has several parts, namely internal, external, and common parts. Plaques occur in the internal section as well as in the common blood vessels of the carotid artery. Hence, plaques create a thicker wall in these vessels, and can be measured as intima-media thickness (IMT). Thus, carotid IMT is used as a risk marker for early heart disease prediction. It can be done by measuring the difference between the lumen-intima (LI) and media adventitia (MA) walls [23].

Recently, many applications regarding ultrasound image segmentation using machine learning and deep learning techniques have been implemented to detect atherosclerosis. The work proposed in Nagaraj [24] used support vector machines (SVM) to train and segment IMT using a dataset of 49 images and resulted in 93% accuracy. Other researchers (see Biswas, Saba, et al. [25]) have worked on a screening tool that integrates a two-stage AI model for IMT and carotid plaque measurements, and consists of a convolutional neural network (CNN) and a fully convolutional network (FCN). The system goes through two deep learning models. The first divides the common carotid artery from the ultrasound images into two categories: the rectangular wall and non-wall patches. Then, the region of interest is analyzed and fed to the second stage, which identifies features in order to calculate the carotid IMT and the plaque total.

The work in Biswas, Kuppili, et al. [26] combined both the CNN algorithm and the machine learning based regression technique for IMT segmentation. Further, Menchón-Lara and colleagues [27] implemented an autoencoder model for segmenting IMT. Their method included regions of interest (ROI) prediction and then LI interface (LII) and MA interface (MAI) walls predictions in the predicted ROI. The authors reported that they used extreme learning machines along with autoencoders to distinguish between the block included in the ROI and the excluded from the ROI. The LII and MAI recognition was done using pixel classification.

      2.5.2 AI in Electrocardiography

      Electrocardiography has a high impact in detecting abnormalities in heart rhythm. Most of the existing machine learning applications based on ECG focus on classification of electrical signals to spot abnormal activity in the heart. With today’s availability of ECG in small portable devices such as smartwatches, AI integration helps patients to monitor their heartbeat levels and detect any abnormalities that might require seeing a doctor or having further tests.

The work of Li et al. [28] applied a multichannel, multiscale deep neural network model to perform ECG classification of normal and abnormal signals, and their method achieved 96.02% accuracy. Other research work by Rajput el. [29] developed an automated ECG signal detection specifically for detecting the severity of hypertension, a high-risk factor for heart disease. Their method included using a two-band optimal biorthogonal wavelet bank filter as well as machine learning techniques to classify the wavelets into low, high-risk hypertension, and healthy. The average classification accuracy for this method was found to be 99.95%, and highlights the ability of this system to be deployed in clinics.

A method for ECG identification and detection of congestive heart failure (CHF) and arrhythmia (ARR) using deep learning techniques is that proposed by Eltrass et al. [30]. It mainly uses both CNN and constant-Q non-stationary Gabor transform (CQ-NSGT), which mainly transforms the 1D ECG signal to a 2D time-frequency representation. The output is then fed to a pretrained CNN model (AlexNet) to extract the features of these signals and then the features are used to diagnose the case by either classifying it as CHF, ARR, or normal sinus rhythm (NSR). The work achieved 98.82% accuracy, 98.87% sensitivity, 99.21% specificity, and 99.20% precision.

In the work by Moridani et al. [31], the authors experimented with multiple machine learning algorithms for ECG signal classification. They concentrated on performing preprocessing techniques for the initial signal and then applying feature extraction for both linear and nonlinear features for the heart rate variability as well as the application of statistical characteristics for the signal. Afterward, they used multilayer perceptron (MLP) and SVM, along with all features as well as optimal features to classify normal and abnormal signals in separate experiments. Their outcomes highlighted the performance of the SVM model that was able to get the highest accuracy while using all features and optimal features. For the optimal features, it achieved 98.3% accuracy, 99.10% sensitivity, and 97.5% specificity.

      2.5.3 AI in CT

      CT scans are mainly used to view parts of the body in detail. In the case of a cardiac CT scan, it displays the heart and the blood vessels clearly which helps experts to diagnose or detect any abnormality. CT scans can detect the early signs of heart disease by scanning the heart’s arteries for any calcified plaque formation to create a coronary artery calcium (CAC) score, which has been proven to be a strong predictor for CVDs (Figure 2.9).