Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography

Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image

Adults with coronary artery disease often have atherosclerosis, this is defined as the accumulation of plaque in the tissues of the arterial wall. Cardiologists utilize optical coherence tomography (OCT), a light-based imaging method, to examine the layers of intracoronary tissue along pathological formations, such as plaque accumulation. Intracoronary cross-sectional images produced by state-of-the-art catheter-based imaging scheme have 10-15 µm high resolution. Nevertheless, interpretation of the obtained images depends on the operator, which takes a lot of time and is exceedingly error-prone from one observer to another. OCT image post-processing that automatically and accurately tags coronary plaques can help the technique become more widely used and lower the diagnostic error rate. To overcome these problems, Atherosclerosis plaque tissue classification using Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Network (APC-OCTPI-SACVAGAN) is proposed which classifies the Atherosclerosis plaque images as Fibro calcific plaque, Fibro atheroma, Thrombus, Fibrous plaque and Micro-vessel. The proposed APC-OCTPI-SACVAGAN technique is executed in MATLAB. The efficiency of proposed APC-OCTPI-SACVAGAN method attains 16.19 %, 17.93 %, 19.81 % and 1.57 % higher accuracy; 16.92 %, 11.54 %, 5.29 % and 1.946 % higher Area under curve; and 28.06 %, 25.32 %, 32.19 % and 39.185 % lower computational time comparing to the existing methods respectively.

Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography

This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm³, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Cardiovascular disease detection and analysis using ultrasonic imaging expels errors in manual clinical trials with precise outcomes. It requires a combination of smart computing systems and intelligent image processors. The disease characteristics are analyzed based on the configuration and precise tuning of the processing device. In this article, a characteristic extraction technique (CET) using knowledge learning (KL) is introduced to improve the analysis precision. The proposed method requires optimal selection of disease features and trained similar datasets for improving the characteristic extraction. The disease attributes and accuracy are identified using the standard knowledge update. The image and data features are segmented using the variable processor configuration to prevent false rates. The false rates due to unidentifiable plaque characteristics result in weak knowledge updates. Therefore, the segmentation and data extraction are unanimously performed to prevent feature misleads. The knowledge base is updated using the extracted and identified plaque characteristics for consecutive image analysis. The processor configurations are manageable using the updated knowledge and characteristics to improve precision. The proposed method is verified using precision, characteristic update, training rate, extraction ratio, and time factor.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.

Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare

Atherosclerosis screening helps the medical model transform from therapeutic medicine to preventive medicine by assessing degree of atherosclerosis prior to the occurrence of fatal vascular events. Pervasive screening emphasizes atherosclerotic monitoring with easy access, quick process, and advanced computing. In this work, we introduced five cutting-edge pervasive technologies including imaging photoplethysmography (iPPG), laser Doppler, radio frequency (RF), thermal imaging (TI), optical fiber sensing and piezoelectric sensor. IPPG measures physiological parameters by using video images that record the subtle skin color changes consistent with cardiac-synchronous blood volume changes in subcutaneous arteries and capillaries. Laser Doppler obtained the information on blood flow by analyzing the spectral components of backscattered light from the illuminated tissues surface. RF is based on Doppler shift caused by the periodic movement of the chest wall induced by respiration and heartbeat. TI measures vital signs by detecting electromagnetic radiation emitted by blood flow. The working principle of optical fiber sensor is to detect the change of light properties caused by the interaction between the measured physiological parameter and the entering light. Piezoelectric sensors are based on the piezoelectric effect of dielectrics. All these pervasive technologies are noninvasive, mobile, and can detect physiological parameters related to atherosclerosis screening.