Citations

Despite the publication of several national/international guidelines, the optimal management of patients with asymptomatic carotid stenosis (AsxCS) remains controversial. This article compares 3 recently released guidelines (the 2020 German–Austrian, the 2021 European Stroke Organization [ESO], and the 2021 Society for Vascular Surgery [SVS] guidelines) vs the 2017 European Society for Vascular Surgery (ESVS) guidelines regarding the optimal management of AsxCS patients. The 2017 ESVS guidelines defined specific imaging/clinical parameters that may identify patient subgroups at high future stroke risk and recommended that carotid endarterectomy (CEA) should or carotid artery stenting (CAS) may be considered for these individuals. The 2020 German–Austrian guidelines provided similar recommendations with the 2017 ESVS Guidelines. The 2021 ESO Guidelines also recommended CEA for AsxCS patients at high risk for stroke on best medical treatment (BMT), but recommended against routine use of CAS in these patients. Finally, the SVS guidelines provided a strong recommendation for CEA+BMT vs BMT alone for low-surgical risk patients with >70% AsxCS. Thus, the ESVS, German–Austrian, and ESO guidelines concurred that all AsxCS patients should receive risk factor modification and BMT, but CEA should or CAS may also be considered for certain AsxCS patient subgroups at high risk for future ipsilateral ischemic stroke.

Object We sought to determine the safety and efficacy in secondary stroke prevention of carotid endarterectomy (CEA) in patients with symptomatic non-stenotic carotid artery disease (SyNC). Methods This was a single-centre retrospective case series. All patients who underwent CEA for unilateral anterior circulation cerebrovascular events with ipsilateral <50% carotid stenosis from 2002 to 2020 were included. Imaging hallmarks including the degree of luminal stenosis and the presence of various vulnerable plaque characteristics (eg, intraplaque haemorrhage (IPH) on MR angiography, ulceration or low-density plaque on CT angiography) were assessed. The presence of vulnerable plaque characteristics was compared between arteries ipsilateral to the ischaemic event and contralateral arteries. The prevalence of perioperative/intraoperative complications, as well as recurrent ischaemic events at follow-up was determined. Results Thirty-two patients were included in the analysis, of which 25.0% were female. Carotid arteries ipsilateral to an ischaemic event had a significantly higher prevalence of IPH when compared with contralateral arteries (80.0% vs 0.0%; p<0.001). There were no intraoperative complications. One patient (3.1%) developed symptoms of transient ipsilateral ischaemia 1 day following CEA which resolved without treatment. In a median follow-up of 18.0 months (IQR 5.0–36.0), only one patient (3.1%) experienced a transient neurologic deficit with complete resolution (annualised rate of recurrent stroke after CEA of 1.5% for a total follow-up of 788 patient-months following CEA). All other patients (31/32, 96.9%) were free of recurrent ischaemic events. Conclusion CEA appears to be safe and well-tolerated in patients with SyNC. Additional studies with larger cohorts and longer follow-up intervals are needed in order to determine the role of CEA in this patient population.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Carotid plaque vulnerability features beyond the degree of stenosis may play a key role in the pathogenesis and recurrence of ischemic cerebrovascular events. This study sought to compare intraplaque hemorrhage (IPH) as a marker of plaque vulnerability in symptomatic patients with mild (<50%), moderate (50%–69%), and severe (≥70%) carotid artery stenosis. We included patients who experienced ischemic cerebrovascular events with no other identifiable sources and underwent carotid endarterectomy for mild (n=32), moderate (n=47), and severe (n=58) carotid artery stenosis. The degree of stenosis and imaging hallmarks were assessed by computed tomography angiography or magnetic resonance angiography. Plaque specimens were stained with hematoxylin and eosin and Movat pentachrome staining. Carotid plaques of patients with mild stenosis had a higher extent of IPH (%) on tissue analysis compared with patients with moderate (mild, 15.7% [interquartile range, 7.8%–26.7%]; moderate, 3.9% [0.0%–9.2%]; P <0.001) and severe carotid artery stenosis (mild, 15.7% [interquartile range, 7.8%–26.7%]; severe, 2.5% [interquartile range, 0.0%–11.2%]; P <0.001). When considering the degree of carotid artery stenosis as a continuous variable, a lower lumen narrowing was associated with higher extent of IPH ( P <0.001; R, −0.329). Our major finding is the association of IPH with mild carotid artery stenosis based on histological analysis. The current study may suggest that IPH potentially plays a role in the mechanism of stroke in patients with nonobstructive carotid stenosis.

The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background Using computed tomography angiography to differentiate between components of carotid atherosclerotic lesions remains largely elusive. This study sought to validate a semi-automated software for computed tomography angiography plaque analysis using histologic comparisons. Materials and Methods A retrospective review was performed of consecutive patients that underwent a carotid endarterectomy, with pre-procedural computed tomography angiography imaging of the cervical arterial vasculature available for review. Images were evaluated using a commercially-available software package, which produced segmented analyses of intraplaque components (e.g. intraplaque hemorrhage, lipid-rich necrotic core, and calcifications). On imaging, each component was assessed in terms of its (1) presence or absence, and (2) both volume and proportion of the total plaque volume (if present). On histological evaluation of carotid endarterectomy specimens, each component was evaluated as an estimated proportion of total plaque volume. Results Of 80 included patients, 30 (37.5%) were female. The average age was 69.7 years (SD = 9.1). Based on imaging, intraplaque hemorrhage was the smallest contributor to plaque composition (1.2% of volumes on average). Statistically significant linear associations were noted between the proportion of intraplaque hemorrhage, lipid-rich necrotic core, and calcifications on histology and the volume of each component on imaging ( p values ranged from 0.0008 to 0.01). Area under curve were poor for intraplaque hemorrhage and lipid-rich necrotic core (0.59 and 0.61, respectively) and acceptable for calcifications (0.73). Conclusion Semi-automated analyses of computed tomography angiography have limited diagnostic accuracy in the detection of intraplaque hemorrhage and lipid-rich necrotic core in carotid artery plaques. However, volumetric imaging measurements of different components corresponded with histologic analysis.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

In this work, we have proposed an on-line computer-aided diagnostic system called “UroImage” that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.