Search Results (85)

Despite significant advances in understanding and management, cardiovascular diseases remain the leading cause of mortality worldwide. Historically, diagnostic and therapeutic strategies have typically targeted obstructive coronary arteries. However, growing evidence supports the pivotal role of non-obstructive mechanisms in myocardial ischemia, prompting a new classification that distinguishes Acute Myocardial Ischemic Syndromes from Non-Acute Myocardial Ischemic Syndromes. In this evolving context, Optical Coherence Tomography (OCT) plays an important diagnostic role in the assessment of both obstructive and non-obstructive ischemic mechanisms. In Acute Myocardial Ischemic Syndromes, OCT enables the identification of major plaque destabilization mechanisms and contributes to the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries, helping to differentiate between atherosclerotic and non-atherosclerotic causes. In Non-Acute Myocardial Ischemic Syndromes, OCT assists in evaluating stenosis severity, plaque morphology, vulnerability, and healing, and may contribute to the diagnosis of Ischemia with Non-Obstructive Coronary Arteries, identifying myocardial bridge and epicardial spasm alongside conventional functional assessment of intermediate stenoses. This narrative review outlines the expanding clinical applications of OCT across the full spectrum of ischemic syndromes, emphasizing its role in bridging obstructive and non-obstructive pathophysiology and supporting a more comprehensive diagnostic approach to ischemic heart disease. Full article

Background: The objective was to evaluate and compare the efficacy of direct fragment impaction, indirect reduction through ligamentotaxis, and the combination of both techniques in burst fractures of the thoracolumbar (TL) spine. Methods: The fractures were categorized using the Arbeitsgemeinschaft für Osteosynthesefragen (AO) classification and assessed via standard computed tomography (CT) scans for spinal canal area (SCA) and mid-sagittal diameter (MSD). The Frankel classification was used to assess neurological deficits. Only single vertebrae AO types A3 and A4 thoracic or lumbar fractures were included. All patients received bisegmental posterior stabilization, one of the reduction techniques, and, if neurological deficits were present, a spinal decompression. Mean preoperative (µSCApre/µMSDpre), postoperative (µSCApost/µMSDpost) and difference (∆SCA/∆MSD) in radiographic values were obtained and analyzed using the Mumford formula. The significance of the reduction from preoperative stenosis was assessed using a t-test, while the effectiveness of the reduction techniques was compared using the Kruskal–Wallis test and Dunn’s post hoc test. The manuscript was focused primarily on radiographic outcomes; therefore, aside from the neurostatus, no other clinical parameters were statistically analyzed. Results: Thirteen patients (38.2%) received stand-alone indirect reduction, 13 patients (38.2%) underwent direct reduction, and a combined reduction was used in eight patients (23.6%). All methods resulted in a statistically significant reduction in spinal canal stenosis (p < 0.05), with a minimal mean ∆SCA of 19%. Patients in the direct reduction group had significantly higher µSCApre values compared to those in the indirect reduction group (p = 0.02). Conclusions: All of the tested reduction techniques provided a significant reduction in spinal canal stenosis. Patients who underwent mere direct reduction had significantly higher preoperative spinal canal stenosis compared to the indirect reduction group. Full article

Background/Objectives: Degenerative lumbar spinal stenosis (LSS) is a prevalent cause of disability in elderly populations, often treated with decompressive surgery. However, postoperative functional outcomes are variable and influenced by factors beyond neural compression alone. This study aimed to investigate the prognostic significance of the Goutallier Classification System (GS), a radiological index of paravertebral muscle fatty degeneration, in predicting long-term postoperative disability and pain in elderly patients undergoing decompression for LSS. Methods: A retrospective cohort study was conducted on 100 elderly patients who underwent primary lumbar decompression surgery for LSS between January 2020 and July 2022, with a minimum two-year follow-up. Patients were stratified according to their preoperative GS grades assessed via MRI. The Oswestry Disability Index (ODI) and Visual Analog Scale (VAS) for pain were collected preoperatively and at follow-up. Changes in the ODI and VAS (ΔODI and ΔVAS) were analyzed to evaluate associations between GS grades and functional outcomes. Results: Significant improvements in the ODI (from 41.0 ± 17.5 to 16.9 ± 8.2) and VAS (from 6.23 ± 2.52 to 3.75 ± 2.38) were observed postoperatively (p < 0.01). However, higher GS grades were associated with greater residual disability and pain at follow-up, as well as with smaller postoperative improvements in these scores (p < 0.01 for ODI; p = 0.01 for VAS). Gender differences were noted, with females predominating in higher GS grades. No significant differences in comorbidities or complication rates were identified across GS subgroups. Conclusions: Preoperative paravertebral muscle degeneration, as measured by the GS, emerged as a significant predictor of postoperative disability and pain in elderly LSS patients. Incorporating GS assessment into preoperative planning may refine surgical risk stratification and inform shared decision-making to optimize long-term functional recovery. Full article

Background: Atherosclerotic disease is the leading global cause of death, driven by progressive plaque accumulation in the arteries. Ultrasound (US) imaging, both conventional (CUS) and intravascular (IVUS), is crucial for the non-invasive assessment of atherosclerotic plaques. Deep learning (DL) techniques have recently gained attention as tools to improve the accuracy and efficiency of image analysis in this domain. This paper reviews recent advancements in DL-based methods for the segmentation, classification, and quantification of atherosclerotic plaques in US imaging, focusing on their performance, clinical relevance, and translational challenges. Methods: A systematic literature search was conducted in the PubMed, Scopus, and Web of Science databases, following PRISMA guidelines. The review included peer-reviewed original articles published up to 31 January 2025 that applied DL models for plaque segmentation, characterization, and/or quantification in US images. Results: A total of 53 studies were included, with 72% focusing on carotid CUS and 28% on coronary IVUS. DL architectures, such as UNet and attention-based networks, were commonly used, achieving high segmentation accuracy with average Dice similarity coefficients of around 84%. Many models provided reliable quantitative outputs (such as total plaque area, plaque burden, and stenosis severity index) with correlation coefficients often exceeding R = 0.9 compared to manual annotations. Limitations included the scarcity of large, annotated, and publicly available datasets; the lack of external validation; and the limited availability of open-source code. Conclusions: DL-based approaches show considerable promise for advancing atherosclerotic plaque analysis in US imaging. To facilitate broader clinical adoption, future research should prioritize methodological standardization, external validation, data and code sharing, and integrating 3D US technologies. Full article

Objective: This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. Methods: We retrospectively enrolled 954 patients with cervical spine magnetic resonance imaging (MRI) data and medical records from the Fifth Affiliated Hospital of Sun-Yat Sen University. The Kang grading method for sagittal MR images of the cervical spine and the spinal cord compression ratio for horizontal MR images of the cervical spine were adopted for cervical spinal canal stenosis grading. The collected data were randomly divided into training/validation and test sets. The training/validation sets were processed by various image preprocessing and annotation methods, in which deep learning convolutional networks, including classification, target detection, and key point localization models, were applied. The predictive grading of the test set by the model was finally contrasted with the grading results of the clinicians, and correlation analysis was performed with the clinical manifestations of the patients. Result: The EfficientNet_B5 model achieved a five-fold cross-validated accuracy of 79.45% and near-perfect agreement with clinician grading on the test set (κ= 0.848, 0.822), surpassing resident–clinician consistency (κ = 0.732, 0.702). The model-derived compression ratio (0.45 ± 0.07) did not differ significantly from manual measurements (0.46 ± 0.07). Correlation analysis showed moderate associations between model outputs and clinical symptoms: EfficientNet_B5 grades (r = 0.526) were comparable to clinician assessments (r = 0.517, 0.503) and higher than those of residents (r = 0.457, 0.448). Conclusion: CNN models demonstrate strong performance in the objective, consistent, and efficient grading of cervical spinal stenosis severity, offering potential clinical value in automated diagnostic support. Full article

Coronary artery disease (CAD) remains a major cause of cardiovascular morbidity and mortality, with growing evidence linking immune dysregulation to its pathogenesis. Aortic stenosis often coexists with CAD (ASCAD), representing an advanced disease form. This study investigates immune pathways in isolated CAD (iCAD) and ASCAD. For this purpose, peripheral blood from 72 individuals (healthy donors, iCAD, and ASCAD patients) was analysed via flow cytometry to assess immune populations. Circulating cytokine levels were measured, and machine learning models identified predictive immune biomarkers. Our data showed that both iCAD and ASCAD patients exhibited immune dysregulation, with reduced dendritic cells, basophils, NK cells, B cells, and T cells, alongside lower frequencies of DCs, lymphocytes, CD8+CD28+ T cells, and CD57+ T cells. Elevated IL-15 and fractalkine, but reduced IL-8 and MCP-1, suggest impaired monocyte and neutrophil mobilisation due to immune cell sequestration in vascular lesions. Distinct immune features emerged between iCAD and ASCAD. iCAD patients showed heightened immune activation, with increased inflammatory CD14+CD16+ monocytes, higher Treg frequencies, and greater CD4+ T cell differentiation into TEM and TEMRA phenotypes. In contrast, ASCAD patients exhibited pronounced immunosenescence, with higher neutrophil counts, lymphopenia, and increased NK and T cell cytotoxicity. Our predictive model distinguished iCAD from ASCAD with high accuracy, identifying CD4+ T cell memory subsets and CD57 expression as key discriminators. This study reveals iCAD as being driven by immune activation and ASCAD by immunosenescence and cytotoxicity. These insights advance CAD immunopathology understanding and support immune-based classification, particularly for ASCAD, where treatment remains limited to surgical intervention. Full article

Bicuspid aortic valve (BAV) is the most common congenital heart anomaly, affecting an estimated 0.5% to 0.77% of the general population. This condition occurs when the aortic valve has only two cusps instead of the usual three, disrupting normal valve function and increasing the risk of various cardiovascular diseases. Often asymptomatic in its early stages, BAV can gradually progress, leading to stenosis, valve insufficiency, and abnormalities of the ascending aorta. One particularly concerning aspect is its potential association with sudden cardiac death (SCD). The aim of this literature review is to examine the relationship between BAV and the risk of SCD, highlighting the pathogenic variants and pathophysiological mechanisms involved while emphasizing the significance of valve classification and its clinical implications. Additionally, it explores current research gaps and future directions to enhance early identification of at-risk individuals and reduce the incidence of SCD. Full article

Background: Intracranial atherosclerotic stenosis (ICAS) is a leading cause of ischemic stroke, particularly in the anterior circulation. Understanding the underlying stroke mechanisms is essential for guiding personalized treatment strategies. This study proposes an integrated framework that combines CT perfusion imaging, vascular anatomical features, computational fluid dynamics (CFD), and machine learning to classify stroke mechanisms based on the Chinese Ischemic Stroke Subclassification (CISS) system. Methods: A retrospective analysis was conducted on 118 patients with intracranial atherosclerotic stenosis. Key indicators were selected using one-way ANOVA with nested cross-validation and visualized through correlation heatmaps. Optimal thresholds were identified using decision trees. The classification performance of six machine learning models was evaluated using ROC and PR curves. Results: Time to Maximum (Tmax) > 4.0 s, wall shear stress ratio (WSSR), pressure ratio, and percent area stenosis were identified as the most predictive indicators. Thresholds such as Tmax > 4.0 s = 134.0 mL and WSSR = 86.51 effectively distinguished stroke subtypes. The Logistic Regression model demonstrated the best performance (AUC = 0.91, AP = 0.85), followed by Naive Bayes models. Conclusions: This multimodal approach effectively differentiates stroke mechanisms in anterior circulation ICAS and holds promise for supporting more precise diagnosis and personalized treatment in clinical practice. Full article

Background and Clinical Significance: Men who engage in anal fisting may experience full rectal and colon thickness injury resulting in an endoscopic emergency. The endoscopist does not routinely question patients about their sexual habits, nor are patients compliant with counseling during the endoscopy procedure as indicated by the infectious disease clinician. Case Presentation: A 47-years-old HIV- and monkeypox virus (MPXV)-negative Caucasian gay man underwent colonoscopy because of changes in bowel habits with anal discomfort and rectal bleeding. The first colonoscopy showed a vegetative annular neoformation of the anal canal. There was a concentric stenosis of the lumen. The endoscopist suspected the diagnosis of anal squamous cell carcinoma and a histopathology investigation was requested. Biopsy histology excluded a frank neoplasm or anal intraepithelial neoplasia (AIN). Then, the patient was referred to a multidisciplinary team. With adequate counseling, the patient disclosed his habitual anal fisting. Laboratory identification of L1–L3 Chlamydia trachomatis (CT) genovars was positive for CT L1, L2, real-time PCR for Neisseria gonorrhoeae (NG), and Mycoplasma hominis. Human Papillomavirus (HPV)-DNA detection identified HPV type 70, 68, and 61. We illustrate this case with plenty of histology and immunohistochemistry. We also review the differential diagnosis of AIN according to the 5th edition (2019) WHO Classification of Digestive System Tumours. Conclusions: Our patient emphasizes two important aspects of endoscopy and pathology: first, the significance of understanding patients’ sexual behaviors in diagnosing rectal and colon injuries, as well as the need for sexually transmitted infections (STI) screening especially for CT; and second, the effectiveness of a multidisciplinary communication model that encourages private discussions to alleviate patients’ fears and improve prevention efforts. Full article

Background: There is a large overlap in the risk profiles and pathophysiologies of coronary artery disease (CAD) and cerebrovascular macroangiopathy. Therefore, this study aimed to analyse whether findings in CAD examination by coronary angiography or cardio-computer tomography (cardio-CT) are predictive of cerebrovascular macroangiopathy. Methods: Our study was a prospective, bicentric, cross-sectional cohort study. A total of 191 patients without earlier CAD diagnosis who underwent a cardio-CT scan or coronary angiography for the screening of CAD during clinical routine were serially included. Two groups were formed based on the criterion of CAD (yes/no), and both were subsequently examined using sonography of the carotids. The CAD scores Syntax score I, Agatston equivalent score, and CAD-RADS score as well as AHA classification were determined. In cerebrovascular examinations, plaques and stenoses of the internal carotid artery (ICA) and the intima-media thickness (IMT) of the common carotid artery were analysed. Demographic and medical data such as the presence of arterial hypertension, diabetes mellitus, obesity, nicotine abuse, and dyslipidaemia were documented. The primary endpoint was the nominal association between CAD and ICA stenosis controlled for age and gender; secondary endpoints were correlations between ICA stenoses and CAD scores. Results: Of the 191 serially recruited patients (58% male, 65 ± 11 yrs.), 101 fulfilled CAD criteria; 90 did not. Of all patients, 137 had ICA plaques, and 11 thereof had an ICA stenosis ≥ 50%. No association was found between CAD and ICA stenosis (Wald = 0.24; p = 0.624). Accordingly, there was no association between IMT and Syntax score I (Wald = 0.38; p = 0.706), Agatston equivalent score (Wald = 0.89; p = 0.380), CAD-RADS score (Wald = 0.90; p = 0.377), or AHA classification (Wald = 0.21; p = 0.837). Common cardiovascular risk factors, i.e., arterial hypertension (Wald = 4.47; p = 0.034), diabetes mellitus (Wald = 7.61; p = 0.006), and nicotine abuse (Wald = 0.83; p = 0.028), were associated with newly diagnosed CAD but not with ICA plaques, stenosis, or increased IMT. Conclusions: In our cohort, newly diagnosed CAD was associated with typical risk factors. However, neither CAD nor these risk factors were associated with cerebrovascular disease. This suggests that in patients without prior CAD diagnosis, findings from CAD examinations might not be reliably predictive of cerebrovascular disease. Full article

Background/Objectives: Peripheral artery disease (PAD) is a significant global health challenge, affecting millions worldwide. Among its various manifestations, femoropopliteal atherosclerotic disease presents a unique challenge due to the biomechanical stresses on the superficial femoral artery (SFA) and popliteal artery (PA). Despite advancements in endovascular interventions, restenosis and stent fractures remain critical issues, particularly in complex and long lesions. Biomimetic stents, such as the SUPERA interwoven nitinol stent, have been developed to address these challenges by closely replicating the natural mechanical properties of the femoropopliteal arteries. This study evaluates the clinical and procedural outcomes of biomimetic stent implantation in patients with femoropopliteal atherosclerotic disease, focusing on patency rates, procedural success, and major adverse limb events (MALE). Methods: A cohort study was conducted at the University Clinical Center of Serbia, including 294 patients with femoropopliteal stenosis or occlusion treated with the SUPERA stent from January 2017 to December 2024. Patients were stratified by lesion complexity using the GLASS classification and procedural success, patency rates, and MALE incidence were assessed. Kaplan–Meier survival analysis was used to evaluate long-term outcomes, and Cox regression analysis identified predictors of MALE. Results: Primary patency rates at 1, 6, 12, and 24 months were 95.6%, 90.1%, 84.2%, and 77.7%, respectively. Primary-assisted patency and secondary patency rates remained high over time. Patients with GLASS IV lesions exhibited significantly lower patency rates and higher MALE incidence compared to GLASS I-III patients (p = 0.002). Occlusion length (≥16 cm) and lesion complexity (GLASS IV) were independent predictors of MALE (p = 0.015). The stent demonstrated high procedural success and durability, with minimal complications. Conclusions: Biomimetic SUPERA stents provide high patency rates and favorable clinical outcomes in complex femoropopliteal lesions. However, lesion complexity and occlusion length significantly impact long-term success. The findings highlight the importance of careful patient selection and lesion assessment for optimizing endovascular treatment strategies in PAD management. Full article

Background: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accurate prediction of adverse events can enhance patient management and improve outcomes. Aim: This study aimed to develop a machine learning model to predict early safety outcomes in patients with severe aortic stenosis undergoing TAVI. Methods: We conducted a retrospective single-centre study involving 224 patients with severe aortic stenosis who underwent TAVI. Seventy-seven clinical and biochemical variables were collected for analysis. To handle unbalanced classification problems, an adaptive synthetic (ADASYN) sampling approach was used. A fined-tuned random forest (RF) machine learning model was developed to predict early safety outcomes, defined as all-cause mortality, stroke, life-threatening bleeding, acute kidney injury (stage 2 or 3), coronary artery obstruction requiring intervention, major vascular complications, and valve-related dysfunction requiring repeat procedures. Shapley Additive Explanations (SHAPs) were used to explain the output of the machine learning model by attributing each variable’s contribution to the final prediction of early safety outcomes. Results: The random forest model identified left femoral artery diameter and aortic valve calcification volume as the most influential predictors of early safety outcomes. SHAPs analysis demonstrated that smaller left femoral artery diameter and higher aortic valve calcification volume were associated with poorer early safety prognoses. Conclusions: The machine learning model highlights of early safety outcomes after TAVI. These findings suggest that incorporating these variables into pre-procedural assessments may improve risk stratification and inform clinical decision-making to enhance patient care. Full article

Background: Elderly patients with severe aortic stenosis (AS) need individualized decision-making in their management in order to benefit in terms of survival and improvement of quality of life. Frailty, a common condition in elderly patients, needs to be considered when weighing treatment options. Aim: We aimed to evaluate outcomes including survival and functional parameters according to disability criteria at six years of follow-up in an older population treated for severe AS using a frailty-based management. Methods: We evaluated data derived from a pilot clinical project involving elderly patients with severe AS referred to a tailored management based on classification by Fried’s score into pre-frail, early frail, and frail and a multidimensional geriatric assessment. A Frailty, Inflammation, Malnutrition, and Sarcopenia (FIMS) score was used to predict the risk of mortality at six years of follow-up. Functional status was evaluated by telephonic interview. Results: At six years of follow-up, we found a survival rate of 40%. It was higher in the pre-frail patients (long rank < 0.001) and in the patients who underwent TAVR treatment (long rank < 0.001). The cut-off FIMS score value of ≥1.28 was an independent determinant associated with a higher risk of mortality at six years of follow-up (HR 2.91; CI 95% 1.7–5.1; p-value 0.001). We found a moderate increase of disability levels, malnutrition status, comorbidities, and number of drugs, but none of them self-reported advanced NYHA class III–IV heart failure. Conclusion: An accurate clinical–instrumental and functional geriatric evaluation in an elderly population with AS is required for a non-futile interventional treatment in terms of survival and functional status even in long-term follow-up. Full article

Coronary artery stenosis detection remains a challenging task due to the complex vascular structure, poor quality of imaging pictures, poor vessel contouring caused by breathing artifacts and stenotic lesions that often appear in a small region of the image. In order to improve the accuracy and efficiency of detection, a new deep-learning technique based on a coronary artery stenosis detection framework (DCA-YOLOv8) is proposed in this paper. The framework consists of a histogram equalization and canny edge detection preprocessing (HEC) enhancement module, a double coordinate attention (DCA) feature extraction module and an output module that combines a newly designed loss function, named adaptive inner-CIoU (AICI). This new framework is called DCA-YOLOv8. The experimental results show that the DCA-YOLOv8 framework performs better than existing object detection algorithms in coronary artery stenosis detection, with precision, recall, F1-score and mean average precision (mAP) at 96.62%, 95.06%, 95.83% and 97.6%, respectively. In addition, the framework performs better in the classification task, with accuracy at 93.2%, precision at 92.94%, recall at 93.5% and F1-score at 93.22%. Despite the limitations of data volume and labeled data, the proposed framework is valuable in applications for assisting the cardiac team in making decisions by using coronary angiography results. Full article

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of $0.92$ for Accuracy and $0.85$ for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of $0.97$ from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems. Full article