Search Results (2,167)

Background: Oral ulceration with bone sequestration (OUBS) is an uncommon clinical lesion characterized by painful mucosal ulceration associated with exposed and necrotic bone in the oral cavity, often without known factors inducing osteonecrosis. Despite its potential for diagnostic confusion with more serious lesions, such as medication-related osteonecrosis, OUBS remains poorly understood and underreported. Objectives: To systematically review the literature on OUBS and identify its main clinical and radiographic characteristics to assist in early diagnosis and appropriate management. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in the PubMed, Scopus, and Web of Science databases on 27 January 2024, to identify case reports, case series, and observational studies that described OUBS. Data extraction focused on demographic information, lesion location, clinical features, radiographic findings, possible etiological factors, management, and outcomes. The Joanna Briggs Institute (JBI) critical appraisal tools were utilized to assess the quality of the case reports and series. Data were synthesized narratively due to heterogeneity among studies. Results: Fifty-seven patients from 22 studies were included. The male-to-female ratio was 2.5:1, with most cases (71.9%) occurring in males. The mean age was 50.22 ± 11.49 years, with the highest incidence in patients aged 50–59 years (64.9%). Most cases were localized to the mandible (94.7%). Clinically, lesions were shallow ulcers (mean size: 0.75 ± 0.85 cm). Pain was the most common symptom (88.9%). Radiographs revealed no significant maxillary abnormalities, but some cases showed radiopaque areas in the mandible. Surgical interventions were most common (40.4%), with complete healing in 67.7% of cases within 4 weeks. Limitations include the predominance of case reports and series, which limits the strength of evidence. Conclusions: Recognizing OUBS can prevent misdiagnosis and unnecessary interventions. Further studies are needed to clarify its etiology and natural history. Registration: This review was registered on the PROSPERO database (registration number CRD42024541416). Full article

Background and Objective: Fatigue represents a hallmark symptom in Multiple Sclerosis (MS), but its diagnosis and clinical evaluation is difficult because it is described as a subjective feeling of exhausted physical and mental sensation. Studies have also shown that approaches based on assisted therapies and robotics, as well as the use of vibration, which are used to improve sensory integration, reduce fatigue. The primary outcome in this study is to evaluate the effects of the application of focal vibrations on the reduction in fatigue, muscle strength, and endurance in MS patients with moderate disability. The secondary outcome is to assess the effects on quality of life, cognitive status, and mood. Methods: We enrolled 40 MS patients. The study was designed as a parallel randomized controlled trial: 20 patients were assigned to the experimental group (EG), who received vibration training, and 20 to the control group (CG), who received traditional physical exercise. Results: We found significant differences in the EG in fatigue, motor, and cognitive outcome and improvement of some aspects of quality of life (QoL). There are correlations between perceived multidimensional fatigue and cadence, step length, and health quality of life composite. Conclusions: Our study demonstrated the potential effectiveness of vibration training in balance, walking endurance, and reduction in the risk of falls in patients with Multiple Sclerosis. In addition, we added evidence about fatigue, non-motor outcomes, in particular promoting mental and physical QoL and individual life satisfaction. The name of the registry is clinicaltrial.gov; the number of registration id NCT05783999; and the date of registration is 14 March 2023. Full article

Chronic pancreatitis (CP) is a progressive condition that is associated with severe complications. Diagnosis of late CP is easy due to characteristic clinical presentation and pathognomonic imaging findings, such as pancreatic calcifications. Early changes, such as lobularity and a dilated main pancreatic duct, are very subtle and challenging to detect with ultrasonography (US) or even computed tomography (CT). Data were accumulating on the usefulness of EUS in the early diagnosis of CP. The sensitivity values for detecting early CP (ECP) by US, MRI, and EUS were 67–69%, 77–78%, and 81–84%, respectively. The specificity values for detecting ECP by US, MRI, and EUS were 90–98%, 83–96%, and 90–100%, respectively. Pancreatic cancer (PDAC) is one of the leading cancers worldwide, with increasing morbidity. Due to its poor prognosis and survival, early diagnosis is crucial. For this indication, EUS also shows better outcomes compared to other imaging methods, especially in tumors < 2 cm. The sensitivity and specificity for diagnosing PDAC with MRI and EUS were 52.3–93%, 77.1–89%, 72–100%, and 90%, respectively. In addition, EUS can detect precancerous conditions that are associated with a higher risk of PDAC. EUS-assisted new techniques, like elastography and contrast enhancement, facilitate the diagnosis of pancreatic lesions and make it even more accurate. Early PDAC changes, such as main pancreatic duct dilatation or irregular margins of pancreatic solid masses, may be detected with EUS. This review describes the efficacy of different imaging techniques in the early detection of CP and PDAC. In addition, we describe the useful interventions made possible by early diagnosis of PDAC and CP. Full article

Objectives: The purpose of the study is to evaluate the diagnostic accuracy of artificial intelligence (AI) software in detecting a common set of periodontal and restorative conditions, including marginal bone loss, dental caries, periapical lesions, calculus, endodontic treatment, crowns, restorations, and open crown margins, using intraoral periapical radiographs. Additionally, the study will assess how this AI software influences the diagnostic accuracy of dentists with varying levels of experience in identifying these conditions. Methods: A total of three hundred digital IOPARs representing 1030 teeth were selected based on predetermined selection criteria. The parameters assessed included (a) calculus, (b) periapical radiolucency, (c) caries, (d) marginal bone loss, (e) type of restorative (filling) material, (f) type of crown retainer material, and (g) detection of open crown margins. Two oral radiologists performed the initial diagnosis of the selected radiographs and independently labeled all the predefined parameters for the provided IOPARs under standardized conditions. This data served as reference data. A pre-trained AI-based computer-aided detection (“CADe”) software (Second Opinion^®, version 1.1) was used for the detection of the predefined features. The reports generated by the AI software were compared with the reference data to evaluate the diagnostic accuracy of the AI software. In the second phase of the study, thirty dental interns and thirty dental specialists were randomly selected. Each participant was randomly assigned five IOPARs and was asked to detect and diagnose the predefined conditions. Subsequently, all the participants were requested to reassess the IOPARs, this time with the assistance of the AI software. All the data was recorded using a self-designed Performa. Results: The sensitivity of the AI software in detecting caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 91.0%, 86.6%, 97.1%, 82.6%, 89.3%, 93.4%, 80.2%, and 91.1%, respectively. The specificity of the AI software in detected caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 87%, 98.3%, 99.6%, 91.9%, 96.4%, 99.3%, 97.8%, and 93.1%, respectively. The differences between the AI software and radiologist diagnoses of caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss were statistically significant (all p values < 0.0001). The results showed that the diagnostic accuracy of operators (interns and specialists) with AI software revealed higher accuracy, sensitivity, and specificity in detecting caries, PA lesions, restoration, endodontic treatment, calculus, and marginal bone loss compared to that without using AI software. There were variations in the improvements in the diagnostic accuracy of interns and dental specialists. Conclusions: Within the limitations of the study, it can be concluded that the tested AI software has high accuracy in detecting the tested dental conditions in IOPARs. The use of AI software enhanced the diagnostic capabilities of dental operators. The present study used AI software to detect a clinically useful set of periodontal and restorative conditions, which can help dental operators in fast and accurate diagnosis and provide high-quality treatment to their patients. Full article

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) is enhancing clinical precision, streamlining workflows, and redefining dental education. This review examines the evolution, applications, and future potential of these technologies in modern dental practice. (2) Methods: A narrative literature review was conducted, synthesizing findings from recent studies on digital radiography, CBCT, AI-assisted diagnostics, 3D imaging, and involving simulation tools (AR/VR). Peer-reviewed journal articles, systematic reviews, and clinical studies were analyzed to explore their impact on diagnosis, treatment planning, surgical execution, and training. (3) Results: Digital and 3D imaging modalities have improved diagnostic accuracy and reduced radiation exposure. AI applications enhance image interpretation, automate clinical tasks, and support treatment simulations. AR and VR technologies provide involved, competency-based surgical training and real-time intraoperative guidance. Integrating 3D printing and portable imaging expands accessibility and personalization in care delivery. (4) Conclusions: The integration of CBCT, AI, AR, and VR represents a paradigm shift in dentistry, elevating precision, efficiency, and patient outcomes. Continued research, standardization, and ethical practice will be essential for widespread adoption and maximizing clinical benefits. Full article

Chronic endometritis (CE) is a persistent, often asymptomatic inflammatory condition of the endometrium, increasingly recognized as a potential contributor to infertility and recurrent implantation failure. Despite its clinical significance, CE remains underdiagnosed due to a lack of standardized diagnostic criteria and its subtle clinical presentation. Objective: This review aims to synthesize the current evidence on the pathophysiology, diagnosis, and treatment of CE, highlighting its impact on reproductive outcomes and the effectiveness of therapeutic interventions. A comprehensive literature review was conducted, analyzing 85 peer-reviewed studies published in the last decade, of which 65 were deemed relevant and retained for further analysis. These studies were selected based on their relevance to the pathophysiology, diagnostic methodologies, and treatment outcomes for CE, focusing on their implications for fertility and assisted reproductive technologies (ARTs). The findings suggest that CE is associated with impaired endometrial receptivity, increased inflammatory markers, and reduced implantation and pregnancy rates with ARTs. Histopathological assessment using CD138 immunostaining remains the gold standard for diagnosis, while hysteroscopy and molecular microbiological techniques provide complementary diagnostic value. Antibiotic treatment has been shown to significantly improve implantation rates and pregnancy outcomes, particularly in women with recurrent implantation failure. Emerging therapies, including probiotics and regenerative medicine approaches, are being explored as potential adjuncts to the conventional treatment. Early and accurate diagnosis of CE is essential for optimizing reproductive outcomes. Standardized diagnostic protocols and individualized treatment strategies are crucial for improving implantation success and pregnancy rates in affected women. Future research should focus on refining the diagnostic methods and exploring novel therapeutic options to enhance endometrial health and fertility outcomes. Full article

Background/Objectives: Radiodermitis is an inflammatory or dystrophic skin process caused by the direct action of ionizing radiation. The primary objective was to study two clinical cases. The secondary objective was to propose the foundations of an intelligent system for decision support in complex cases of radiodermitis diagnosis that can operate even in the case of a low amount of available clinical data that can be used for training. Methods: The first case is a female patient, aged 74 years, with squamous cell carcinoma on a chronic radiodermitis site, which appeared after 20 years of local radiotherapy treatment for mammary adenocarcinoma. Dermatologic examination revealed five round-oval nodules between 2 and 8 cm in diameter. They were pink colored with lilac edges, hard and infiltrated on palpation, adherent to the subcutaneous tissue, painless, and located above and lateral on the right chest and the upper region of the right hypochondrium. The second case concerns a 60-year-old patient with verrucous squamous cell carcinoma appearing on a chronic radiodermatitis 40 years after local radio-therapeutic treatment with Chaoul rays for a deep right temporal region mycosis. There are presented artificial intelligence (AI) challenges regarding the application of advanced hybrid models in decision support for diagnosis of difficult radiodermitis cases, in that intelligent computing must be made in the context of very little available data, and collaboration between physicians is necessary. Results: Both cases were confirmed by histology as squamos cell carcinomas. In the AI research, the adaptation of the IntMediSys intelligent system was proposed for solving complex cases of radiodermitis. The proposal integrates different AI technologies, which include agents, intelligent computing, and blackboard systems. Conclusions: The presented first cases confirm the presence of a squamous cell carcinoma that appeared on chronic radiodermitis after a long latency. The foundations of a highly complex collaboration and decision support system that can assist physicians in the radiodermitis diagnostics establishment that opens the path for further development are presented. Full article

Immunoglobulin G4-related disease (IgG4-RD) is a systemic fibroinflammatory condition marked by tumefactive lesions, IgG4+ plasma cell-rich infiltrates, storiform fibrosis, and obliterative phlebitis. Its multisystem involvement and overlap with malignancies, infections, and immune disorders complicate diagnosis despite recent classification advances. This study summarizes diagnostic challenges, highlights the role of histopathology as per the 2019 classification criteria established by the American College of Rheumatology and the European League Against Rheumatism (ACR/EULAR), and explores emerging tools to improve diagnostic accuracy. ACR/EULAR classification emphasizes three cardinal histopathological features (storiform fibrosis, obliterative phlebitis, or dense lymphoplasmacytic infiltrates) combined with an IgG4+/IgG+ plasma cell ratio >40% and organ-specific IgG4+ thresholds. While serum IgG4 levels are often elevated, their poor specificity necessitates confirmatory biopsy. Diagnostic limitations include sampling variability due to patchy fibrosis, interobserver discrepancies in immunohistochemical interpretation, and differentiation from mimics like lymphoma. Emerging solutions incorporate novel biomarkers (plasmablasts, anti-annexin A11) and advanced techniques (flow cytometry, digital pathology). Future research directions should focus on AI-assisted pattern recognition, multi-omics profiling, and organ-specific criteria refinement. While histopathology remains the diagnostic cornerstone, a multidisciplinary approach integrating clinical, radiological, and laboratory data is vital. Innovations in biomarkers promise improved diagnostic accuracy and personalized care, balancing novel advancements with foundational pathological evaluation. Full article

Background/Objectives: This study investigates the application of machine learning (ML) techniques in diagnosing speech sound disorders (SSDs) in Saudi Arabic-speaking children, with a specific focus on phonological biomarkers, particularly Infrequent Variance (InfrVar), to improve diagnostic accuracy. SSDs are a significant concern in pediatric speech pathology, affecting an estimated 10–15% of preschool-aged children worldwide. However, accurate diagnosis remains challenging, especially in linguistically diverse populations. Traditional diagnostic tools, such as the Percentage of Consonants Correct (PCC), often fail to capture subtle phonological variations. This study explores the potential of machine learning models to enhance diagnostic accuracy by incorporating culturally relevant phonological biomarkers like InfrVar, aiming to develop a more effective diagnostic approach for SSDs in Saudi Arabic-speaking children. Methods: Data from 235 Saudi Arabic-speaking children aged 2;6 to 5;11 years were analyzed using several machine learning models: Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes. The dataset was used to classify speech patterns into four categories: Atypical, Typical Development (TD), Articulation, and Delay. Phonological features such as Phonological Variance (PhonVar), InfrVar, and Percentage of Consonants Correct (PCC) were used as key variables. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contributions of individual features to model predictions. Results: The XGBoost and Random Forest models demonstrated the highest performance, with an accuracy of 91.49% and an AUC of 99.14%. SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. The K-Means clustering approach identified four distinct subgroups based on speech development patterns: TD (46.61%), Articulation (25.42%), Atypical (18.64%), and Delay (9.32%). Conclusions: Machine learning models, particularly XGBoost and Random Forest, effectively classified speech development categories in Saudi Arabic-speaking children. This study highlights the importance of incorporating culturally specific phonological biomarkers like InfrVar and PhonVar to improve diagnostic precision for SSDs. These findings lay the groundwork for the development of AI-assisted diagnostic tools tailored to diverse linguistic contexts, enhancing early intervention strategies in pediatric speech pathology. Full article

Introduction: Testicular cancer (TC) is diagnosed at a young age and carries a remarkably high cure rate. Hence, there is a sizeable population living in the survivorship phase. Many studies have highlighted the plight of TC survivors as a result of the late side-effects of the different therapeutic modalities used for the treatment of TC. This is the first study in Ireland to highlight the impact of TC on socio-economic health, sexual health, and fertility in survivors. Method: We performed a questionnaire-based survey, which was fully anonymised to encourage participation. Questionnaires were designed to measure the self-reported impact on social, sexual, and economic health on a five-point Likert scale (ranging from no effect to very significant effect), whereas any effect on fertility was investigated with questions regarding biological children before and after cancer with or without medical assistance. Results: A total of 83 TC survivors participated in the study. Almost half of our respondents revealed some effect on their performance at work and personal finances. Around one-third suffered an impact on career choice, job security, and their relationship with their partner. Regarding sexual health, the worst repercussions were noted on sex drive and body image perception, where close to half of the respondents reported at least some deterioration. Ejaculation and erectile function were affected in 30% of the participants. Of all participants, 17% reported issues with fertility, and the same proportion reported seeking medical help to conceive after diagnosis or treatment of TC. Conclusions: In conclusion, some TC survivors experience significant impact on their socio-economic and sexual health. Full article

The aim of the study was to determine the prevalence rates of respiratory pathogens using syndromic tests and also to show which respiratory viruses were detected in suspected cases, especially during and after the pandemic period. A total of 1984 different respiratory tract samples from various departments were included and studied with the QIAstat-Dx device in 2021–2023. The samples were studied with the QIAstat-Dx1 Respiratory SARS-CoV-2 Panel. The kit used was a fully automated, multiplex syndromic test that detected SARS-CoV-2 and 21 other respiratory tract pathogens. As a result of the study, the prevalence of Rhinovirus/Enterovirus (RV/EV) (18.59%), RV/EV-SARS-CoV-2 (42.74%), SARS-CoV-2 (5.04%), and Influenza A Virus (IAV) (5.59%) agents was found to be higher than other agents during the period investigated. Among the 1984 patients examined, 959 (48.33%) had a single viral agent, 156 (7.86%) had double coinfection, 11 (0.55%) had triple coinfection and 1 patient had quadruple coinfection. Nearly half of the patients had a straightforward infection, which helps clinicians in directing specific treatment methods. The study results demonstrate that during the pandemic period, the detection of respiratory pathogens such as SARS-CoV-2 and RV/EV was not only critical for accurate diagnosis but also served as an important indicator of the broader epidemiological trends in respiratory infections. The seasonal distribution showed that while RV/EV was frequently present, its coinfection with SARS-CoV-2 was notably observed only in the first trimester. In light of our findings showing high rates of SARS-CoV-2 and RV/EV detection, along with diverse patterns of coinfection in clinical samples, such comprehensive testing not only assists in rapid diagnosis but also informs public health strategies by reflecting the evolving landscape of respiratory infections in the pandemic and post-pandemic era. Full article

Background/Objectives: Old age and cognitive impairment/dementia are risk factors for falling and fall-related injuries. We have, in a previous study in persons with cognitive impairment, shown that falls were associated with frailty, reduced physical fitness, and cognitive reduction. Falls were independent of the disorders causing the impaired functions. Because most falls are innocent, knowledge of predictors of fall-related injuries seems more clinically relevant than the predictors of falls. Predictors of falls and fall-related injuries are not necessarily identical. The aim of this follow-up study to our previous one in the same population was to explore predictors of fall-related injuries in fallers and compare these predictors with those of falls. Methods: This study and our previous study used data from the “The Norwegian Registry of Persons Assessed for Cognitive Symptoms” (NorCog), a Norwegian research and quality registry with a biobank. The registry included consecutive home-dwelling persons referred to Norwegian specialist healthcare units for assessment of cognitive decline. This study included 3774 persons from our previous study who experienced falls last year and compared persons with and without a fall-related injury. A fall-related injury was defined as admittance to a hospital for the injury. Results: The annual incidence of fall-related injuries in the fallers was 884/3774 (23.4%). Female sex, older age, lower BMI, in need of public health service and walking assistance, and low Hb and Ca were independent predictors of fall-related injuries, indicating reduced physical fitness and state of health and a high burden of comorbidity. Injuries were not associated with the degree of cognitive impairment or the dementia diagnosis. Conclusions: In home-dwelling persons with impaired cognitive functions and falls, fall-related injuries were associated with reduced physical fitness and state of health. In contrast to predictors of falls, neither the degree of cognitive impairment nor the dementia diagnosis was associated with fall-related injuries. The difference is comprehensible. Persons with cognitive impairment or dementia might have reduced power of judgment and be inattentive, unconcerned and careless, which increases the fall incidence but not the risk of injury once falling. Prevention of fall-related injuries should focus on relieving comorbidities, improving physical fitness and general health rather than on cognitive improvement. Full article

Background/Objectives: Magnetic resonance imaging (MRI) is crucial in detecting suspicious lesions and diagnosing clinically significant prostate cancer (csPCa). However, variability in MRI-targeted diagnostic pathways arises due to factors such as patient characteristics, imaging protocols, and radiologist expertise. Artificial intelligence (AI) offers potential solutions to these challenges by enhancing diagnostic accuracy and efficiency. Methods: This narrative review explores AI techniques, particularly machine learning and deep learning, in the context of prostate cancer diagnosis. It examines their application in improving MRI scan quality, detecting artifacts, and assisting radiologists in lesion detection and interpretation. It also considers how AI helps to reduce reading time and inter-reader variability. Results: AI has demonstrated sensitivity that is generally comparable to experienced radiologists, although specificity tends to be lower, potentially increasing false-positive rates. The clinical impact of these results requires validation in larger, prospective multicenter studies. AI is effective in identifying artifacts, assessing MRI quality, and assisting in diagnostic efficiency by providing second opinions and automating lesion detection. However, variability in study methodologies, datasets, and imaging protocols can impact AI’s generalizability, limiting its broader clinical application. Conclusions: While AI shows significant promise in enhancing diagnostic accuracy and efficiency for csPCa detection, challenges remain, particularly with the generalizability of AI models. To improve AI robustness and integration into clinical practice, multicenter datasets and transparent reporting are essential. Further development, validation, and standardization are required for AI’s widespread clinical adoption. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality, often diagnosed at advanced stages, where minimally invasive tissue sampling is essential for diagnosis and molecular profiling. Rapid On-Site Evaluation (ROSE) enhances the diagnostic yield of small biopsies, but is frequently limited by a shortage of pathologists and logistical constraints. Telepathology offers a potential solution by enabling remote real-time assessment. This study evaluates the feasibility, diagnostic accuracy, and efficiency of telecytology-assisted ROSE (TC-ROSE) using touch imprint cytology (TIC) during CT-guided transthoracic core needle biopsy (CNB) of pulmonary nodules. Methods: 50 patients underwent CNB. TIC samples were assessed and evaluated on-site or remotely via a fully remote-controlled microscope system (OCUS^®). TIC slide preparation was performed by pathologists (30 cases), radiologists (10), and trained assistants (10). The study analyzed diagnostic concordance between remote and on-site assessments, time efficiency, and the feasibility of involving non-pathologists in TIC preparation. Results: Diagnostic samples were obtained in 86% of TIC samples, with full concordance (100%) between TC-ROSE and traditional ROSE. The slides required approximately 140 s for scanning, and the overall evaluation time was around 3 min per case. Overall, 100% of TICs were adequately assessed by both pathologists and non-pathologists. No increased number of complications was recorded among patients with TCROSE, compared to those ROSE evaluated. The remote setup allowed pathologists to maintain routine workflows, improving time efficiency. Conclusions: The findings confirm that telecytology is a viable, accurate, and efficient approach to ROSE, offering a practical solution for overcoming workforce and logistical barriers, particularly in settings with limited pathology resources. Full article

Background/Objectives: Common carotid artery dissection (CCAD) can result in severe neurological sequelae; however, its diagnosis may be challenging due to consciousness disturbance and aphasia. The objective of this article is to propose a new imaging feature to assist in the identification of CCAD. Methods: This retrospective case series enrolled 139 patients with discharge diagnoses of aortic dissection who underwent carotid ultrasound during admission over a period of three years. Results: Among these patients, 23 had type A aortic dissection, and 113 had type B aortic dissection or related conditions. Notably, among the 23 patients with type A aortic dissection, eight had associated common carotid artery dissection (CCAD), and a total of nine CCAD events were identified. Meanwhile, a B-mode ultrasound revealed six double lumens with intimal flaps and three intramural thrombi. The ‘train-line’ pattern in ultrasonography was discerned by detecting a hypoechoic thickened wall, which was characterized by a margin formed by two parallel linear reflections in close proximity. This distinctive “train-line” pattern was identified in three intimal flaps and two intramural thrombi. Conclusions: While double lumens and intramural thrombus are prevalent findings, the latter may be misinterpreted as atherosclerotic plaque. The “train-line” pattern may aid in distinguishing intramural thrombus from atheromatous plaque, offering an additional diagnostic tool alongside the identification of double lumens with intimal flaps. Full article