Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.

PURPOSE OF THE REVIEW: Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. RECENT FINDING: In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue-specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning- and deep learning-based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning-based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.

A Review on Atherosclerotic Biology, Wall Stiffness, Physics of Elasticity, and Its Ultrasound-Based Measurement.

Functional and structural changes in the common carotid artery are biomarkers for cardiovascular risk. Current methods for measuring functional changes include pulse wave velocity, compliance, distensibility, strain, stress, stiffness, and elasticity derived from arterial waveforms. The review is focused on the ultrasound-based carotid artery elasticity and stiffness measurements covering the physics of elasticity and linking it to biological evolution of arterial stiffness. The paper also presents evolution of plaque with a focus on the pathophysiologic cascade leading to arterial hardening. Using the concept of strain, and image-based elasticity, the paper then reviews the lumen diameter and carotid intima-media thickness measurements in combined temporal and spatial domains. Finally, the review presents the factors which influence the understanding of atherosclerotic disease formation and cardiovascular risk including arterial stiffness, tissue morphological characteristics, and image-based elasticity measurement.

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). This study focuses on automated plaque segmentation in the internal carotid artery (ICA) using solo deep learning (SDL) and hybrid deep learning (HDL) models. The methodology consists of a novel design of 10 types of SDL/HDL models (AtheroEdge™ 3.0 systems (AtheroPoint™, Roseville, CA) with a depth of four layers each. Five of the models use cross-entropy (CE)-loss, and the other five models use Dice similarity coefficient (DSC)-loss functions derived from UNet, UNet+, SegNet, SegNet-UNet, and SegNet-UNet+. The K10 protocol (Train:Test:90%:10%) was applied for all 10 models for training and predicting (segmenting) the plaque region, which was then quantified to compute the plaque area in mm2. Further, the data augmentation effect was analyzed. The database consisted of 970 ICA B-mode US scans taken from 99 moderate to high-risk patients. Using the difference area threshold of 10 mm2 between ground truth (GT) and artificial intelligence (AI), the area under the curve (AUC) values were 0.91, 0.911, 0.908, 0.905, and 0.898, all with a p-value of <0.001 (for CE-loss models) and 0.883, 0.889, 0.905, 0.889, and 0.907, all with a p-value of <0.001 (for DSC-loss models). The correlations between the AI-based plaque area and GT plaque area were 0.98, 0.96, 0.97, 0.98, and 0.97, all with a p-value of <0.001 (for CE-loss models) and 0.98, 0.98, 0.97, 0.98, and 0.98 (for DSC-loss models). Overall, the online system performs plaque segmentation in less than 1 s. We validate our hypothesis that HDL and SDL models demonstrate comparable performance. SegNet-UNet was the best-performing hybrid architecture.

A case report of thrombosed varicosities of pubic collateral veins: Ideal treatment strategy and contribution of era imaging technologies in diagnosis.

Collateral circulation is an alternative path occurring in case of venous or artery obstruction. This path may usually develop after primary recanalization. In our case, a 62-year-old woman presented to our Emergency Department complaining about a suprapubic swelling with a cyanotic discoloration of the overlying skin for the past 10 days for which she had been previously prescribed antibiotics. Investigation with ultrasound and contrast-enhanced computed tomography was performed. An imaging study revealed thrombosed pubic varicose collateral veins due to deep vein obstruction and occlusion of the left external iliac vein. The patient was treated with low-molecular-weight heparin, and swelling subsided gradually. Collateral veins of the abdominal wall and over the pubic tubercle are highly predictive of deep venous obstructive disease proximal to the groin level. These collaterals should never be removed, and the patient should be subjected to a diligent laboratory and imaging investigation.

Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients.

AIM: The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSMconv). METHODS: Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA); (b) computing the AAGSM as a function of GSMconv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted. RESULTS: IRB approved, 204 patients' left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ±â€¯11 years; mean HbA1c: 6.12 ±â€¯1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of -0.13, P = 0.01), compared to GSM (CC of -0.06, P = 0.24). The RCCA and LCCA showed CC of -0.18, P < 0.01 and -0.08; P < 0.24. Female and males showed CC of -0.29, P < 0.01 and -0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSMconv. A paired t-test between operators and expert (P < 0.0001); inter-operator CC of 0.85 (P < 0.0001). CONCLUSIONS: Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSMconv.

Extracranial internal carotid artery calcium volume measurement using computer tomography.

BACKGROUND: The extent of calcium volume in the carotid arteries of contrast-based computer tomography (CT) is a valuable indicator of stroke risk. This study presents an automated, simple and fast calcium volume computation system. Since the high contrast agent can sometimes obscure the presence of calcium in the CT slices, it is therefore necessary to identify these slices before the corrected volume can be estimated. METHODS: The system typically consists of segmenting the calcium region from the CT scan into slices based on Hounsfield Unit-based threshold, and subsequently computing the summation of the calcium areas in each slice. However, when the carotid volume has intermittently higher concentration of contrast agent, a dependable approach is adapted to correct the calcium region using the neighboring slices, thereby estimating the correct volume. Furthermore, mitigation is provided following the regulatory constraints by changing the system to semi-automated criteria as a fall back solution. We evaluate the automated and semi-automated techniques using completely manual calcium volumes computed based on the manual tracings by the neuroradiologist. RESULTS: A total of 64 patients with calcified plaque in the internal carotid artery were analyzed. Using the above algorithm, our automated and semi-automated system yields correlation coefficients (CC) of 0.89 and 0.96 against first manual readings and 0.90 and 0.96 against second manual readings, respectively. Using the t-test, there was no significant difference between the automated and semi-automated methods against manual. The intra-observer reliability was excellent with CC 0.98. CONCLUSIONS: Compared to automated method, the semi-automated method for calcium volume is acceptable and closer to manual strategy for calcium volume. Further work evaluating and confirming the performance of our semi-automated protocol is now warranted.

Pathogenesis and management of intradialytic hypertension.

Hypertension is common in chronic kidney disease patients especially in those undergoing hemodialysis (HD). Usually, blood pressure falls after the HD session but in some patients a paradoxical increase has been observed during or immediately after HD. This phenomenon is referred as intradialytic hypertension. HD patients with intradialytic hypertension or increased blood pressure during HD present higher cardiovascular (CV) morbidity and mortality rates. The underlying mechanism of intradialytic hypertension is multifactorial. Activation both of renin-angiotensinaldosterone system (RAAS) and sympathetic nervous system, volume and sodium overload with concomitant increase in cardiac output, and endothelial dysfunction have been implicated in the pathogenesis of intradialytic hypertension. Given the lack of clinical trials regarding the pathophysiology and management of intradialytic hypertension, current treatment strategies are based mainly on experts' opinion. The purpose of this review is to describe the pathophysiology of intradialytic hypertension and discuss current strategies in order to improve intradialytic blood pressure management and concomitant HD patients' outcomes.