Evaluation of Mild Cognitive Impairment through Perientorhinal/Hippocampal Imaging and Comprehensive Neuropsychological and Psychophysical Assessment.

Mild cognitive impairment (MCI) is a significant concern as it is a risk factor for AD progression, and early detection is vital in order to delay dementia onset and enable potential therapeutic interventions. Olfactory impairment is recognized as a predictive biomarker in neurodegenerative processes. The aims of this study were to explore the degree of entorhinal cortical atrophy (ERICA) and the severity of MCI symptoms; to analyze magnetic resonance imaging (MRI) results for the entorhinal cortex, parahippocampal gyrus, peri entorhinal cortex, and the cerebellar tentorium; and to perform a comprehensive neuropsychological and psychophysical assessment. The main results highlighted that in our sample-multidomain amnesic MCI patients with hyposmic symptomatology-we found that ERICA scores were associated with the severity of anxiety symptomatology. One possible hypothesis to explain this observation is that anxiety may contribute to neurodegenerative processes by inducing chronic stress and inflammation. Future research should consider the longitudinal development of neuropsychological scores, anxiety disorders, and brain atrophy to determine their potential predictive value for MCI progression. These findings suggest the importance of psychological factors in MCI progression and the utility of neuropsychological assessment alongside neuroimaging techniques for early detection and follow-up in MCI patients.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Global longitudinal strain by cardiac magnetic resonance is associated with cardiac iron and complications in beta-thalassemia major patients.

BACKGROUND: The aim of this cross-sectional study was to investigate the association of left ventricular (LV) strain parameters with demographics, clinical data, cardiovascular magnetic resonance (CMR) findings, and cardiac complications (heart failure and arrhythmias) in patients with ß-thalassemia major (ß-TM). METHOD: We considered 266 ß-TM patients (134 females, 37.08 ± 11.60 years) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia (E-MIOT) project and 80 healthy controls (50 females, mean age 39.77 ± 11.29 years). The CMR protocol included cine images for the assessment of global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS) using feature tracking (FT) and for the quantification of LV function parameters, the T2* technique for the assessment of myocardial iron overload, and late gadolinium enhancement (LGE) technique. RESULTS: In comparison to the healthy control group, ß-TM patients showed impaired GLS, GCS, and GRS values. Among ß-TM patients, sex was identified as the sole independent determinant of all LV strain parameters. All LV strain parameters displayed a significant correlation with LV end-diastolic volume index, end-systolic volume index, mass index, and ejection fraction, and with the number of segments exhibiting LGE. Only GLS exhibited a significant correlation with global heart T2* values and the number of segments with T2* < 20 ms. Patients with cardiac complications exhibited significantly impaired GLS compared to those without cardiac complications. CONCLUSION: In patients with ß-TM, GLS, GCS, and GRS were impaired in comparison with control subjects. Among LV strain parameters, only GLS demonstrated a significant association with cardiac iron levels and complications.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Unlocking the potential of photon counting detector CT for paediatric imaging: a pictorial essay.

Recent advancements in CT technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual X-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline the potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilizing PCD-CT in comparison to conventional standard energy integrating detector CT.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Hippocampus and olfactory impairment in Parkinson disease: a comparative exploratory combined volumetric/functional MRI study.

INTRODUCTION: Patients with Parkinson's Disease (PD) commonly experience Olfactory Dysfunction (OD). Our exploratory study examined hippocampal volumetric and resting-state functional magnetic resonance imaging (rs-fMRI) variations in a Healthy Control (HC) group versus a cognitively normal PD group, further categorized into PD with No/Mild Hyposmia (PD-N/MH) and PD with Severe Hyposmia (PD-SH). METHODS: We calculated participants' relative Total Hippocampal Volume (rTHV) and performed Spearman's partial correlations, controlled for age and gender, to examine the correlation between rTHV and olfactory performance assessed by the Odor Stick Identification Test for the Japanese (OSIT-J) score. Mann-Whitney U tests assessed rTHV differences across groups and subgroups, rejecting the null hypothesis for p < 0.05. Furthermore, a seed-based rs-fMRI analysis compared hippocampal connectivity differences using a one-way ANCOVA covariate model with controls for age and gender. RESULTS: Spearman's partial correlations indicated a moderate positive correlation between rTHV and OSIT-J in the whole study population (ρ = 0.406; p = 0.007), PD group (ρ = 0.493; p = 0.008), and PD-N/MH subgroup (ρ = 0.617; p = 0.025). Mann-Whitney U tests demonstrated lower rTHV in PD-SH subgroup compared to both HC group (p = 0.013) and PD-N/MH subgroup (p = 0.029). Seed-to-voxel rsfMRI analysis revealed reduced hippocampal connectivity in PD-SH subjects compared to HC subjects with a single cluster of voxels. CONCLUSIONS: Although the design of the study do not allow to make firm conclusions, it is reasonable to speculate that the progressive involvement of the hippocampus in PD patients is associated with the progression of OD.

Special Report on the Consensus QIBA Profile for Objective Analytical Validation of Non-calcified and High-risk Plaque and Other Biomarkers using Computed Tomography Angiography.

RATIONALE AND OBJECTIVES: Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available. Vendors use differing definitions, assessment metrics, and validation data sets to describe their offerings without clinician users having the capability to make objective assessments of accuracy and precision and how this affects diagnostic confidence. MATERIALS AND METHODS: The QIBA Profile for Atherosclerosis Biomarkers by CTA was created by the Quantitative Imaging Biomarkers Alliance (QIBA) to improve objectivity and decrease the variability of noninvasive plaque phenotyping. The Profile provides claims on the accuracy and precision of plaque measures individually and when combined. RESULTS: Individual plaque morphology measurements are evaluated in terms of bias (accuracy), slope (consistency of the bias across the measurement range, needed for measurements of change), and variability. The multiparametric plaque stability phenotype is evaluated in terms of agreement with expert pathologists. The Profile is intended for a broad audience, including those engaged in discovery science, clinical trials, and patient care. CONCLUSION: This report provides a rationale and overview of the Profile claims and how to comply with the Profile in research and clinical practice. SUMMARY STATEMENT: This article summarizes objective means to validate the analytical performance of non-calcified plaque (NCP), other emerging plaque morphology measurements, and multiparametric histology-defined high-risk plaque (HRP), as outlined in the QIBA Profile for Atherosclerosis Biomarkers by CTA.

Rapid Plaque Progression Is Independently Associated With Hyperglycemia and Low HDL Cholesterol in Patients With Stable Coronary Artery Disease: A PARADIGM Study.

BACKGROUND: We assessed whether combinations of cardiometabolic risk factors independently predict coronary plaque progression (PP) and major adverse cardiovascular events in patients with stable coronary artery disease. METHODS: Patients with known or suspected stable coronary artery disease (60.9±9.3 years, 55.4% male) undergoing serial coronary computed tomography angiographies (≥2 years apart), with clinical characterization and follow-up (N=1200), were analyzed from the PARADIGM study (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging). Plaque volumes measured in coronary segments (≥2 mm in diameter) were summed to provide whole heart plaque volume (mm3) and percent atheroma volume (plaque volume/vessel volume×100; %) per patient at baseline and follow-up. Rapid PP was defined as a percent atheroma volume increase of ≥1.0%/y. Major adverse cardiovascular events included nonfatal myocardial infarction, death, and unplanned coronary revascularization. RESULTS: In an interscan period of 3.2 years (interquartile range, 1.9), rapid PP occurred in 341 patients (28%). At multivariable analysis, the combination of cardiometabolic risk factors defined as metabolic syndrome predicted rapid PP (odds ratio, 1.51 [95% CI, 1.12-2.03]; P=0.007) together with older age, smoking habits, and baseline percent atheroma volume. Among single cardiometabolic variables, high fasting plasma glucose (diabetes or fasting plasma glucose >100 mg/dL) and low HDL-C (high-density lipoprotein cholesterol; <40 mg/dL in males and <50 mg/dL in females) were independently associated with rapid PP, in particular when combined (odds ratio, 2.37 [95% CI, 1.56-3.61]; P<0.001). In a follow-up of 8.23 years (interquartile range, 5.92-9.53), major adverse cardiovascular events occurred in 201 patients (17%). At multivariable Cox analysis, the combination of high fasting plasma glucose with high systemic blood pressure (treated hypertension or systemic blood pressure >130/85 mm Hg) was an independent predictor of events (hazard ratio, 1.79 [95% CI, 1.10-2.90]; P=0.018) together with family history, baseline percent atheroma volume, and rapid PP. CONCLUSIONS: In patients with stable coronary artery disease, the combination of hyperglycemia with low HDL-C is associated with rapid PP independently of other risk factors, baseline plaque burden, and treatment. The combination of hyperglycemia with high systemic blood pressure independently predicts the worse outcome beyond PP. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02803411.

Atrial and Ventricular Involvement in Acute Myocarditis Patients with Preserved Ejection Fraction: A Single-Center Cardiovascular Magnetic Resonance Study.

Cardiac magnetic resonance (CMR) is commonly employed to confirm the diagnosis of acute myocarditis (AM). However, the impact of atrial and ventricular function in AM patients with preserved ejection fraction (EF) deserves further investigation. Therefore, the aim of this study was to explore the incremental diagnostic value of combining atrial and strain functions using CMR in patients with AM and preserved EF. This retrospective study collected CMR scans of 126 consecutive patients with AM (meeting the Lake Louise criteria) and with preserved EF, as well as 52 age- and sex-matched control subjects. Left atrial (LA) and left ventricular (LV) strain functions were assessed using conventional cine-SSFP sequences. In patients with AM and preserved EF, impaired ventricular and atrial strain functions were observed compared to control subjects. These impairments remained significant even in multivariable analysis. The combined model of atrial and ventricular functions proved to be the most effective in distinguishing AM patients with preserved ejection fraction from control subjects, achieving an area under the curve of 0.77 and showing a significant improvement in the likelihood ratio. These findings suggest that a combined analysis of both atrial and ventricular functions may improve the diagnostic accuracy for patients with AM and preserved EF.

Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization.

Atherosclerotic plaque buildup in the coronary and carotid arteries is pivotal in the onset of acute myocardial infarctions or cerebrovascular events, leading to heightened levels of illness and death. Atherosclerosis is a complex and multistep disease, beginning with the deposition of low-density lipoproteins in the arterial intima and culminating in plaque rupture. Modern technology favors non-invasive imaging techniques to assess atherosclerotic plaque and offer insights beyond mere artery stenosis. Among these, computed tomography stands out for its widespread clinical adoption and is prized for its speed and accessibility. Nonetheless, some limitations persist. The introduction of photon-counting computed tomography (PCCT), with its multi-energy capabilities, enhanced spatial resolution, and superior soft tissue contrast with minimal electronic noise, brings significant advantages to carotid and coronary artery imaging, enabling a more comprehensive examination of atherosclerotic plaque composition. This narrative review aims to provide a comprehensive overview of the main concepts related to PCCT. Additionally, we aim to explore the existing literature on the clinical application of PCCT in assessing atherosclerotic plaque. Finally, we will examine the advantages and limitations of this recently introduced technology.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Prognostic role of cardiovascular magnetic resonance in Takotsubo syndrome: A systematic review.

BACKGROUND: Takotsubo syndrome (TS) is characterized by transient myocardial dysfunction with outcomes ranging from favorable to life-threatening. Cardiovascular magnetic resonance (CMR) has emerged as an essential tool in its diagnosis and management and is consistently recommended by current guidelines in the diagnostic work-up. However, the prognostic value of CMR in patients with TS remains undetermined. The aim of this study was to assess the prognostic value of CMR in managing patients with TS. METHOD: PubMed, MEDLINE via Ovid, Scopus, and the Cochrane Library were searched to identify studies reporting the prognostic role of multiparameteric CMR in patients with TS with a follow-up ≥ 12 months. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE), defined as all-cause mortality, cardiac death, heart failure, sudden cardiac death, recurrence of TS, and cerebrovascular events. RESULTS: Five studies with 564 patients were included for reporting correlation of CMR parameters with MACCE. Primary endpoint occurred in 69 (12%) patients. Among the CMR parameters assessed, myocardial strain parameters (including measurements of the left atrium, left and right ventricle), right ventricle involvement, and a CMR-based radiomics model demonstrated correlations with MACCE. Additionally, one study showed the predictive ability of a CMR score. CONCLUSION: The current systematic review suggests that CMR may offer prognostic insights in TS patients, underscoring its potential clinical utility for integration into clinical practice. However, scarce data are currently available; hence, further research is needed.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment.

BACKGROUND: Stroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and ML have emerged as promising tools in medical imaging, including neuroradiology. This systematic review and meta-analysis aimed to evaluate the methodological quality of studies employing radiomics for atherosclerotic carotid artery disease analysis and ML algorithms for culprit plaque identification using CT or MRI. MATERIALS AND METHODS: Pubmed, WoS and Scopus databases were searched for relevant studies published from January 2005 to May 2023. RQS assessed methodological quality of studies included in the review. QUADAS-2 assessed the risk of bias. A meta-analysis and three meta regressions were conducted on study performance based on model type, imaging modality and segmentation method. RESULTS: RQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for a single study with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques had a satisfactory performance, with an AUC of 0.85, surpassing clinical models. However, combining radiomics with clinical features yielded the highest AUC of 0.89. Meta-regression analyses confirmed these findings. MRI-based models slightly outperformed CT-based ones, but the difference was not significant. CONCLUSION: In conclusion, radiomics and ML hold promise for assessing carotid plaque vulnerability, aiding in early cerebrovascular event prediction. Combining radiomics with clinical data enhances predictive performance.

How to improve epidemiological trustworthiness concerning abdominal aortic aneurysms.

BACKGROUND: Research on degenerative abdominal aortic aneurysms (AAA) is hampered by complex pathophysiology, sub-optimal pre-clinical models, and lack of effective medical therapies. In addition, trustworthiness of existing epidemiological data is impaired by elements of ambiguity, inaccuracy, and inconsistency. Our aim is to foster debate concerning the trustworthiness of AAA epidemiological data and to discuss potential solutions. METHODS: We searched the literature from the last five decades for relevant epidemiological data concerning AAA development, rupture, and repair. We then discussed the main issues burdening existing AAA epidemiological figures and proposed suggestions potentially beneficial to AAA diagnosis, prognostication, and management. RESULTS: Recent data suggest a heterogeneous scenario concerning AAA epidemiology with rates markedly varying by country and study cohorts. Overall, AAA prevalence seems to be decreasing worldwide while mortality is apparently increasing regardless of recent improvements in aortic-repair techniques. Prevalence and mortality are decreasing in high-income countries, whereas low-income countries show an increase in both. However, several pieces of information are missing or outdated, thus systematic renewal is necessary. Current AAA definition and surgical criteria do not consider inter-individual variability of baseline aortic size, further decreasing their reliability. CONCLUSIONS: Switching from flat aortic-size thresholds to relative aortic indices would improve epidemiological trustworthiness regarding AAAs. Aortometry standardization focusing on simplicity, univocity, and accuracy is crucial. A patient-tailored approach integrating clinical data, multi-adjusted indices, and imaging parameters is desirable. Several novel imaging modalities boast promising profiles for investigating the aortic wall. New contrast agents, computational analyses, and artificial intelligence-powered software could provide further improvements.

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1-205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7-69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

Neuroradiological manifestations in hospitalized patients with COVID-19: An Italian national multicenter study on behalf of AINR (Associazione Italiana di Neuroradiologia) and SIRM (Società Italiana di Radiologia Medica).

PURPOSE: This multicentric study aims to characterize and assess the occurrence of neuroradiological findings among patients with SARS-CoV-2 infection during the first Italian wave of the pandemic outbreak. MATERIALS AND METHODS: Patients' data were collected between May 2020 and June 2020. Clinical and laboratory data, chest imaging, brain CT, and MRI imaging were included. Acquired data were centralized and analyzed in two hospitals: ASST Spedali Civili, Brescia, and IRRCS San Raffaele Research Hospital, Milan, Italy. COVID-19 patients were classified into two different subgroups, vascular and nonvascular. The vascular pattern was further divided into ischemic and hemorrhagic stroke groups. RESULTS: Four hundred and fifteen patients from 20 different Italian Centers were enrolled in the study. The most frequent symptom was focal neurological deficit, found in 143 patients (34.5%). The most frequent neuroradiological finding was ischemic stroke in 122 (29.4%) patients. Forty-four (10.6%) patients presented a cerebral hemorrhage. Forty-seven patients had non-stroke neuroimaging lesions (11.3%). The most common was PRES-like syndrome (28%), SWI hypointensities (22%), and encephalitis (19%). The stroke group had higher CAD risk (37.5% vs 20%, p = .016) and higher D-dimer levels (1875 ng/mL vs 451 ng/mL, p < .001) compared to the negative group. CONCLUSION: Our study describes the biggest cohort study in Italy on brain imaging of COVID-19 patients and confirms that COVID-19 patients are at risk of strokes, possibly due to a pro-thrombotic microenvironment. Moreover, apart from stroke, the other neuroradiological patterns described align with the ones reported worldwide.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.