Test Images Research Articles

Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening. Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors' assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated. In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p=0.50; SEN: 81.8 vs. 88.6, p=0.16; SPE: 89.2 vs. 87.6, p=0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p=0.00015; SEN: 79.5 vs. 74.6, p=0.029; SPE: 81.7 vs. 75.8, p=0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p=0.80; SEN: 84.8 vs. 82.6, p=0.72; SPE: 81.2 vs. 81.2, p>0.99). The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer. This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

Early diagnostic value of ECT whole-body bone imaging combined with PINP and β-CTX for bone metastasis of lung cancer.

This research was aimed at investigating the early diagnostic value of emission computed tomograph (ECT) whole-body bone imaging combined with PINP and β-CTX for bone metastasis of lung cancer. Case data of 86 lung cancer patients were categorized into lung cancer with bone metastasis (LCWBM, 46 cases) and lung cancer without bone metastasis (LCWOBM, 40 cases) groups according to the presence or absence of bone metastasis. Patients' general information were collected. ECT whole-body bone imaging was used to detect bone metastases and the grading of the extent of disease (EOD) in both groups, and electrochemiluminescence was utilized to detect the serum levels of PINP and β-CTX. Spearman correlation analysis was employed to evaluate the correlation between EOD grading and PINP and β-CTX levels. Logistic univariate and multivariate regression was implemented to analyze the risk factors of bone metastasis of lung cancer. Receiver operating characteristic (ROC) curve was applied to analyze the diagnostic efficacy of the single test of ECT whole-body bone imaging, PINP, or β-CTX and the combination of the three tests. The differences in pathological type, clinical stage and EOD grading, the number of positive ECT cases, and the expression levels of PINP and β-CTX between the LCWBM and LCWOBM groups were statistically significant. In LCWBM patients with different EOD grading, the trends of the expression of PINP and β-CTX were grade 3 > grade 2 > grade 1 and grade 0. Further correlation analyses revealed that EOD grading showed a significant positive correlation with the PINP and β-CTX expression levels. Univariate logistic regression analysis demonstrated that adenocarcinoma, TNM stage IV, ECT positivity, and high expression of PINP and β-CTX were associated with bone metastasis of lung cancer, and multivariate logistic regression analysis indicated that ECT positivity, high expression of PINP and β-CTX were independent risk factors for bone metastasis of lung cancer. The area under the curve (AUC) of ECT, PINP, and β-CTX alone for the diagnosis of bone metastasis of lung cancer were 0.872, 0.888, and 0.874, respectively, and the AUC for the combined diagnosis of the three was 0.963, which was greater than that of any one of the individual indices, with a sensitivity of 86.96% and a specificity of 97.50% at a Youden index of 0.845. ECT whole-body bone imaging combined with PINP and β-CTX has high diagnostic value for bone metastasis of lung cancer.

Telemedicine in nephrology: future perspective and solutions.

Telemedicine is a medical practice that uses electronic information and communication technologies. It is not exclusive to face-to-face care but complements face-to-face care and other forms of medical care. It has advantages such as facilitating home therapy, reducing patient travel time and costs, and empowering patients. This makes equitable access to care feasible. Clinical studies have been conducted on telemedicine in nephrology outpatient care, inpatient consultations and hemodialysis, indicating that telemedicine can improve patient satisfaction, leading to enhanced treatment owing to increased adherence and frequency of visits. However, it has not been sufficiently used in the kidney field. The key to spreading "telenephrology" (telemedicine in nephrology) is how physical examinations and laboratory/imaging/physiological tests, currently challenging to perform without face-to-face contact, can be substituted with methods optimized for the telemedicine framework. This paper describes the current status of telemedicine and telenephrology, along with advanced methods for collecting data equivalent to laboratory, imaging and physiological tests outside of hospitals, including estimation of serum creatinine levels from saliva or tear fluid, estimation of blood hemoglobin levels by taking pictures of the eyelid conjunctiva or nails with a smartphone and ultrasound of the kidneys using motion capture technology. With an understanding of the strengths and weaknesses of current telemedicine, we should make full use of it for better treatment and patient care. However, further telenephrology research is required.

Unravelling Chikungunya’s Neurological Toll: Section A Case Report with Insights from Magnetic Resonance Imaging of Brain

Chikungunya, an emerging neurotropic virus, has the potential to induce various neurological abnormalities. When suspecting encephalitis, it is imperative to test for the Chikungunya virus, which requires an early diagnosis and course of treatment. This is a case report of a 54-year-old male who presented with fever, giddiness, ataxia and slurred speech. Magnetic Resonance Imaging (MRI) brain revealed typical hallmark features of chikungunya encephalitis, with an incidental right cerebellopontine angle tumour. A positive serum chikungunya antigen Polymerase Chain Reaction (PCR) validated the diagnosis. The patient was initially managed in the Intensive Care Unit (ICU), showing a dramatic response to the treatment, and then shifted to the medicine ward for further observation and management. This case underscores the importance of considering chikungunya in patients presenting with encephalitis differential diagnosis, especially in endemic regions like India. Comprehensive imaging and PCR testing were instrumental in diagnosis. Treatment included supportive care, intravenous antibiotics, antivirals and steroids, leading to significant improvement. By day 12, notable recovery was observed, and follow-up MRI brain showed no new abnormalities. This case adds to the evidence of severe neurological manifestations of Chikungunya virus, highlighting the need for early recognition and management to improve outcomes. The concurrent presentation of chikungunya encephalitis and a cerebello-pontine angle tumour emphasises the necessity for thorough neuroimaging and PCR testing in suspected cases. The patient’s dramatic recovery following timely intervention demonstrates the potential for positive outcomes with appropriate management.

Integration of plain woven carbon fiber yarn with 304 wire mesh reinforment on synthetic hybrid composite

This work focuses on a hybrid composite comprising a plain carbon fiber yarn weaved on a stainless steel wire mesh, bonded with epoxy resin (LY556) and hardener (HY951). The composite consists of a middle layer of wire mesh with carbon fiber yarns oriented at 90° and 45°, and incorporates carbon fiber mats into the top and bottom layers. The hand layup method was employed to stack these materials, and various mechanical tests were conducted to assess their properties. The composite with a 45° carbon fiber yarn plain-weaved on wire mesh had better mechanical performance than the 90° orientation. It had a maximum tensile stress of 83.63 MPa, which is 18.09 % higher than the 90° orientation. It also had 19.17 % higher flexural strength and 15.78 % higher toughness in impact tests. Additionally, field emission scanning electron microscope (FESEM) imaging and X-ray diffraction tests (XRD) were utilized to further analyze the composite’s structure and properties

Genetic Biomarkers in Heart Failure: From Gene Panels to Polygenic Risk Scores.

This review aims to provide a comprehensive overview of the current understanding of genetic markers associated with heart failure (HF) and its underlying causative diseases, such as cardiomyopathies. It highlights the relevance of genetic biomarkers in diagnosing HF, predicting prognosis, potentially identifying its preclinical stages and identifying targets to enable the implementation of individualized medicine approaches. The prevalence of HF is increasing due to an aging population but with greater access to disease-modifying therapies. Advanced diagnostic tools such as cardiac magnetic resonance, nuclear imaging, and AI-enabled diagnostic testing are now being utilized to further characterize HF patients. Additionally, the importance of genetic testing in HF diagnosis and management is increasingly being recognized. Genetic biomarkers, including single nucleotide polymorphisms (SNPs) and rare genetic variants, are emerging as crucial tools for diagnosing HF substrates, determining prognosis and increasingly directing therapy. These genetic insights are key to optimizing HF management and delivering personalized treatment tailored to individual patients. HF is a complex syndrome affecting millions globally, characterized by high mortality and significant economic burden. Understanding the underlying etiologies of HF is essential for improving management and clinical outcomes. Recent advances highlight the use of multimodal assessments, including AI-enabled diagnostics and genetic testing, to better characterize and manage HF. Genetic biomarkers are particularly promising in identifying preclinical HF stages and providing personalized treatment options. The genetic contribution to HF is heterogeneous, with both monogenic and polygenic bases playing a role. These developments underscore the shift towards personalized medicine in HF management.

Lecanemab Planning: Blueprint for Safe and Effective Management of Complex Therapies.

Approximately 6.9 million American individuals have Alzheimer dementia and 50% have mild disease. Lecanemab, an approved antiamyloid antibody, is associated with modest reduction in functional decline in patients with mild dementia or mild cognitive impairment. In Clarity-AD, 239 (26.6%) of patients experienced amyloid-related imaging abnormalities (ARIAs) overall (i.e., ARIAs associated with hemorrhages or edema). The complexity of treatment and risks of adverse events necessitate a multidisciplinary collaborative approach. With limited treatment options, lecanemab approval generated significant interest among clinicians, patients, and families. Lecanemab treatment requires biweekly infusions along with ongoing imaging tests, laboratory monitoring, patient assessment, drug interaction screening, and cognitive function monitoring. Processes to support patient selection, access, and safety are important given the monitoring requirements and total cost of care. The planning process for lecanemab can serve as a blueprint to support safe and effective management of therapeutic innovation in neurology and other areas.

Review of nuclear medicine imaging used in medical diagnostics

In this review, we will provide an overview of the role of nuclear medicine in Medical Diagnostics. Nuclear medicine is defined as the diagnosis and treatment of disease using radiolabeled compounds known as radiopharmaceuticals. Nuclear imaging produces images by detecting radiation from different parts of the body after a radioactive tracer material is administered. The images are recorded on a computer and film. Different types of nuclear imaging tests have different preparation instructions. Nuclear imaging is used primarily to diagnose or treat illnesses. Conditions diagnosed by nuclear medicine imaging include: Blood disorders, Thyroid disease. including hypothyroidism, Heart disease, Gallbladder disease, Lung problems, Bone problems, and kidney disease. Nuclear medicine, in combination with radiological modalities, gives extra information for diagnosis, prognosis, staging, treatment management, and the evaluation of responses to therapy in a non-invasive manner.

Real-world diagnostic, referral, and treatment patterns in early Alzheimer's disease among community-based practices in the United States.

Over 90% of individuals with mild cognitive impairment (MCI) may not receive a timely diagnosis. Understanding community-based practice patterns, where most individuals are seen, is critical to improving patient care. To understand how patients with MCI and mild dementia due to Alzheimer's disease (AD) are diagnosed and managed in community-based settings, including the use of clinical and cognitive assessments, referrals to dementia-related specialties, and receipt of treatment. This observational study recruited community-based primary care physicians (PCPs) (N = 177) and neurologists (N = 147) in August-September 2023, through a verified physician panel with broad geographic representation across the US. Physicians abstracted medical chart data from patients diagnosed with MCI or mild AD within the previous two years. Data collected included use of neurocognitive assessments, biomarker and structural imagine tests, referrals, and treatments. Descriptive statistics were used. Medical records for 817 MCI and 467 mild AD patients were abstracted. The mean age was 70.2 years, 56.4% were female, and 67.2% were White. Symptoms were commonly reported by a family member (67.2%). Nearly 1 in 6 patients did not receive any neurocognitive assessments (16.1%), and nearly 1 in 4 did not receive a structural imaging or AD-specific biomarker test (23.7%). AD-specific biomarker tests were more common among patients aged ≥65 (87.1% versus 75.3%; p < 0.05). Less than 1 in 4 patients were referred for cognitive/behavioral concerns. As the diagnostic and treatment landscape changes, education on symptom recognition, and physician training on new technologies may facilitate timely diagnoses and improve patient outcomes.

Accurate Differential Diagnosis of Cardiomyopathy Phenotype Based on Multimodal AI Technology

Cardiomyopathy is a group of diseases involving the heart muscle, mainly manifested by the abnormality of the structure and function of the heart muscle. According to etiology and pathological characteristics, cardiomyopathy can be divided into various types, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), non-dilated left ventricular cardiomyopathy (NDLVC) and restrictive cardiomyopathy (RCM). These different types of cardiomyopathy differ significantly in clinical presentation, pathophysiological mechanisms, and treatment strategies, so accurately differentiating the different phenotypes of cardiomyopathy is critical to developing a personalized treatment plan. However, traditional diagnostic methods such as electrocardiogram (ECG), echocardiography, magnetic resonance imaging (MRI) and genetic testing, while able to provide useful information to a certain extent, still have limitations in complex cases and can lead to misdiagnosis or missed diagnosis. In order to overcome the limitations of traditional diagnostic methods and improve the ability of accurate diagnosis of different phenotypes of cardiomyopathy, this study developed a cardiomyopathy phenotypic differential diagnosis system based on multimodal artificial intelligence (AI) technology. We integrate patient data from multiple centers, including clinical data, electrocardiograms (ECG), echocardiograms, magnetic resonance imaging (MRI), and genomic data, standardized and pre-processed. Through machine learning and deep learning algorithms, multimodal AI models are constructed and trained using large-scale training data sets. The accuracy and stability of the model in the differentiation of different cardiomyopathy phenotypes were verified on an independent data set, and the model parameters were optimized according to the verification results to improve the generalization ability and clinical applicability of the model.

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for plate counting and content identification algorithm comparison, using standard evaluation metrics. The approach utilized the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. The model is trained on labeled image data, and its performance is assessed using a precision-recall curve at a confidence threshold of 0.5, achieving a mean average precision (mAP) of 0.873, indicating robust overall performance. The weight estimation procedure combines computer vision techniques to measure food volume using both RGB and depth cameras. Subsequently, density models specific to each food type are applied to estimate the detected food weight. The estimation model's parameters are calibrated through experiments that generate volume-to-weight conversion tables for different food items. Validation of the system was conducted using rice and chicken, yielding error margins of 5.07% and 3.75%, respectively, demonstrating the feasibility and accuracy of the proposed method.

Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods

Forest fires have important ecological, social and economic consequences causing loss of life and property. In order to prevent these consequences, it is very important to intervene in active fires in a timely manner and to determine the extent of burnt areas as soon as possible. In such studies, remote sensing methods provide great benefits in terms of speed and cost. In recent years, various methods have been developed to segment active fires and burnt areas with satellite images. Deep learning methods successfully perform segmentation processes in many areas such as disease detection in the field of health, crop type determination in the field of agriculture, land use and building detection in the field of urbanization. In this study, a method that can automatically identify active fires and burnt areas in terms of location and area size using mono-temporal Sentinel 2 satellite images with deep learning methods was developed. In particular, a new training and validation set was created to train the convolutional neural network (CNN) based on the U-Net+InceptionResNetV2 architecture. The model applied on the test data has been shown to give successful results with an overall accuracy of 0.97 and an IoU (Intersection over union) value of 0.88 in the detection of burnt areas, and an overall accuracy of 0.99 and an IoU value of 0.82 in the detection of active fires. Finally, when the test images that were not used in the training dataset were evaluated with the trained model, it was revealed that the results were quite consistent in the detection of active fires and burnt areas and their geographical locations.

Liquid Biopsy in Hepatocellular Carcinoma: Challenges, Advances, and Clinical Implications.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional's skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Evaluation of diagnostic criteria for mild-to-advanced stages of Fontan-associated liver disease: A nationwide epidemiological survey in Japan.

Fontan-associated liver disease (FALD) is a complication after Fontan surgery, and a common cause of liver tumors and cirrhosis. However, no diagnostic criteria for FALD have been established, leading to an underestimation of its prevalence. We conducted a national survey to elucidate the characteristics of FALD by collecting data from high-volume centers managing patients who had undergone the Fontan surgery in Japan. In total, 1168 patients were enrolled in the study. First, we examined typical liver findings on ultrasonography after the Fontan surgery. Next, we proposed diagnostic criteria for FALD and advanced FALD based on blood tests, imaging, liver tumors, and pathological examinations. We investigated the sensitivity of histologically diagnosed FALD and advanced FALD based on criteria for blood or imaging tests. Hepatomegaly, hepatic venous dilatation, caudate lobe enlargement, splenomegaly, liver atrophy, ascites, hepatocellular carcinoma, and hepatic tumors other than hepatocellular carcinoma were observed in 37.7%, 29.9%, 18.4%, 33.2%, 3.2%, 6.0%, 0.85%, and 10.0% of patients, respectively. Typical ultrasound findings of FALD included hepatomegaly, hepatic vein dilatation, and splenomegaly, reflecting liver congestion. With the progression of fibrosis, caudate lobe enlargement and splenomegaly became more prominent. Based on these findings, we proposed diagnostic criteria for FALD. Using these criteria, FALD was diagnosed in 1014 (86.8%) of the patients, and all patients with a pathological diagnosis of FALD were successfully identified. Eight patients were found to have pathological cirrhosis, and all were diagnosed with advanced FALD using our criteria based on blood tests or imaging. Our diagnostic criteria facilitate detection of FALD or advanced FALD after the Fontan surgery. The accuracy of these criteria should be further evaluated.

The Implication of NT-proBNP in the Assessment of the Clinical Phenotype of Patients with Type 2 Diabetes Mellitus, Without Established Cardiovascular Disease

Background: Natriuretic peptide (NP) levels have been proposed for characterization and risk stratification of heart failure (HF) among patients with cardiovascular disease (CVD). However, their role in patients with diabetes mellitus type 2 (T2DM) has not been well studied and understood. The aim of this study was to assess phenotypical, functional characteristics and imaging parameters in relation to N-terminal pro b-type natriuretic peptide (NT-proBNP) values in T2DM patients without known CVD that may predispose to overt HF. Methods: This was a cross-sectional study of 100 consecutive T2DM patients (mean overall age of 67 ± 9 years, 40% women and 60% men) who were enrolled from the outpatient diabetic clinic. Patients underwent a cardiopulmonary exercise test (CPET), and echocardiographic and cardiac magnetic resonance imaging (CMR); serum NT-proBNP was also measured. Results: The mean (standard deviation) NT-proBNP was 149 (±186) pg/mL. Patients in the highest tertile of NT-proBNP values (&gt;107 pg/mL) had lower values of predicted maximum oxygen consumption compared to the lowest quartile (&lt;55 pg/mL) (84% vs. 92%, p = 0.018) in the CPET and higher ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/e′) (9.0 vs. 7.2, p = 0.05) in echocardiography. Finally, there was a negative correlation between right ventricle end diastolic volume in CMR and predicted maximum oxygen consumption (b = −0.225 ± 0.11, p = 0.046). Conclusions: NT-proBNP levels seemed to be a useful marker in people with T2DM, as elevated levels reflected ongoing appearance of HF with preserved ejection fraction and were related to CPET and echocardiographic indices of impaired left ventricular diastolic and right ventricular systolic function.

Subtle signs of atrial cardiomyopathy and left ventricular diastolic dysfunction are associated with reduced cognitive function: results from the Hamburg City Health Study.

Atrial fibrillation is associated with cognitive dysfunction. Atrial cardiomyopathy has been correlated with both entities. We aimed to characterize the association of echocardiographic parameters of atrial cardiomyopathy with cognitive function and cerebral changes. Participants of the population-based Hamburg City Health Study underwent in-depth transthoracic echocardiography and cognitive function testing, the Animal Naming Test (ANT), the Trail Making Test A (TMT-A) and B (TMT-B), 10-word learning test and cerebral magnetic resonance imaging. After excluding individuals with stroke or depression, data from 7852 individuals were available. In multi-variable-adjusted regression analyses, the E/e'-ratio was associated with the level of impairment in the ANT (-0.19 per one standard deviation [SD] increase, 95% confidence interval [CI] -0.36-[-0.01]) and the TMT-A (0.01 per one SD increase, 95% CI 0.003-0.020). Left atrial global peak strain was associated with positive performance in the TMT-A and B (-0.01 per one SD increase [95% CI -0.02-(-0.002)] and -0.02 per one SD increase [95% CI -0.03-(-0.01)], respectively) and the immediate recall of the 10-word learning test (0.11 per one SD increase, 95% CI 0.02-0.20). The E/e'-ratio was positively associated with the total and periventricular white matter hyperintensity load in age- and sex-adjusted regression analyses though statistical significance was lost after multi-variable adjustment. Subclinical echocardiographic signs of atrial cardiomyopathy and left ventricular diastolic dysfunction are associated with impaired performance in cognitive tests in the population. Our data provide evidence of the clinically important cardio-cerebral axis, relating cardiac dysfunction with cognitive performance.

Eosinophilic gastroenteritis: a rare cause of ileitis.

Eosinophilic gastroenteritis (EG) is a rare disorder characterised by eosinophilic infiltration in the gastrointestinal tract. The disease most commonly occurs in the stomach, but can affect all sections and layers of the gastrointestinal tract, causing non-specific changes and symptoms that may delay diagnosis. A high index of clinical suspicion and diferent laboratory and imaging tests, including colonoscopy with biopsy, are essential for correct diagnosis.

Performance optimization of vessel sealing using a hemostatic ultrasonic scalpel.

Using ultrasound technology as one of the therapeutic methods, in which ultrasound waves of different frequencies and intensities are employed, has significantly contributed to enhancing and facilitating the treatment process of various diseases. A Hemostatic Ultrasonic Scalpel can entail considerable advantages by simultaneously performing two operations tissue cutting and coagulation of biological tissues. In the present study, employing experimental design through response surface methodology, the effect of ultrasonic power and the duration of vibration application on the tissue has been investigated. Two parameters, namely the burst pressure of the sealed vessel and the length of the thermal seal zone, were measured by pressure testing and analysis image of the thermal effect region at the sealed vessel area, respectively. The pressure test results demonstrated that an input power of 52 W and the application of vibrations for 8 s under a constant force of 10 N, showed the optimized maximum burst pressure equal to 1100 mmHg. Examination of the sealed vessel images revealed a linear increase in thermal damage with increasing input power.

Porous Metal Backing for High-Temperature Ultrasonic Transducers

Improving the performance of high-temperature ultrasonic transducers is a goal of major importance in many industrial applications. To this aim, we propose to use porous metals that support high temperatures as backings. Thus, the acoustic properties of stainless steel and porous stainless steel with porosity of 25% and 35% are determined at ambient temperature and up to 400 °C. Over the temperature range, the longitudinal wave velocity variation is comprised between 5% and 6% in the porous metals. We find that temperature does not significantly affect the attenuation in the material. The pulse-echo response and frequency response of a LiNbO3-based transducer with a porous backing are simulated using a one dimensional electroacoustic model. These simulations, compared to those of a reference transducer, show that the axial resolution with such a design allows these transducers to be used for imaging and/or Non-Destructive Testing and evaluation at high temperature.

Noninvasive Imaging Test to Assess Liver Fibrosis: Vibration-controlled Transient Elastography

Liver fibrosis refers to the formation of scar tissue in the liver when inflammation persists over a long period. Assessing liver fibrosis is crucial for predicting the prognosis of chronic liver disease and managing patients with these conditions. Although a liver biopsy remains the gold standard for assessing liver fibrosis, it is limited by its invasive nature. Consequently, continuous efforts have been made to develop non-invasive methods for evaluating liver fibrosis, including imaging techniques and serum biomarkers. Vibration-controlled transient elastography (VCTE), a representative non-invasive imaging technique, has been used widely for liver fibrosis assessment since its introduction in 2003. This paper discusses the principles and methods of measurement, the advantages and disadvantages, and the considerations for interpreting VCTE based on the 2024 KASL Clinical Practice Guidelines for Non-invasive Tests to Assess Liver Fibrosis in Chronic Liver Disease. In addition, the diagnostic utility of VCTE in chronic viral hepatitis is reviewed.