Predictive value of anthropometric and biochemical indices in non-alcoholic fatty pancreas disease: a cross-sectional study.

OBJECTIVES: Triglyceride (TG), triglyceride-glucose index (TyG), body mass index (BMI), TyG-BMI and triglyceride to high-density lipoprotein ratio (TG/HDL) have been reported to be reliable predictors of non-alcoholic fatty liver disease. However, there are few studies on potential predictors of non-alcoholic fatty pancreas disease (NAFPD). Our aim was to evaluate these and other parameters for predicting NAFPD. DESIGN: Cross-sectional study design. SETTING: Physical examination centre of a tertiary hospital in China. PARTICIPANTS: This study involved 1774 subjects who underwent physical examinations from January 2016 to September 2016. PRIMARY AND SECONDARY OUTCOME MEASURES: From each subject, data were collected for 13 basic physical examination and blood biochemical parameters: age, weight, height, BMI, TyG, TyG-BMI, high-density lipoprotein (HDL), low-density lipoprotein, total cholesterol, TG, fasting plasma glucose, TG/HDL and uric acid. NAFPD was diagnosed by abdominal ultrasonography. A logistic regression model with a restricted cubic spline was used to evaluate the relationship between each parameter and NAFPD. The receiver operating characteristic (ROC) curve was used to calculate the area under the curve for each parameter. RESULTS: HDL was negatively correlated with NAFPD, height was almost uncorrelated with NAFPD and the remaining 11 parameters were positively correlated with NAFPD. ROC curve showed that weight-related parameters (weight, BMI and TyG-BMI) and TG-related parameters (TyG, TG and TG/HDL) had high predictive values for the identification of NAFPD. The combinations of multiple parameters had a better prediction effect than a single parameter. All the predictive effects did not differ by sex. CONCLUSIONS: Weight-related and TG-related parameters are good predictors of NAFPD in all populations. BMI showed the greatest predictive potential. Multiparameter combinations appear to be a good way to predict NAFPD.

Multimodal ultrasound imaging: a method to improve the accuracy of sentinel lymph node diagnosis in breast cancer.

Aim: This study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound. Methods: Preoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results. Results: Among the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p<0.05), while the remaining three methods had no statistically significant differences (p>0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients. Conclusion: Multimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy.

Benchmarking Supervised and Self-Supervised Learning Methods in A Large Ultrasound Muti-task Images Dataset.

Deep learning in ultrasound(US) imaging aims to construct foundational models that accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets and narrow task types have restricted this field in recent years. To address these challenges, we introduce US-MTD120K, a multi-task ultrasound dataset with 120,354 real-world two-dimensional images. This dataset covers three standard plane recognition and two diagnostic tasks in ultrasound imaging, providing a rich basis for model training and evaluation. We detail the data collection, distribution, and labelling processes, ensuring a thorough understanding of the dataset's structure. Furthermore, we conduct extensive benchmark tests on 27 state-of-the-art methods from both supervised and self-supervised learning(SSL) perspectives. In the realm of supervised learning, we analyze the sensitivity of two main feature computation methods to ultrasound images at the representational level, highlighting that models which judiciously constrain global feature computation could potentially serve as a viable analytical approach for US image analysis. In the context of self-supervised learning, we delved into the modelling process of self-supervised learning models for medical images and proposed an improvement strategy, named MoCo-US, a solution that addresses the excessive reliance on pretext task design from the input side. It achieves competitive performance with minimal pretext task design and enhances other SSL methods simply. The dataset and the code will be available at https://github.com/JsongZhang/CDOA-for-UMTD.

Hydrochloric Acid-Induced Acute Lung Injury Models: Dynamic Change and Quantitative Analysis of Modified Lung Ultrasound Scoring System and High-Resolution Computed Tomography.

OBJECTIVE: Acute lung injury (ALI) has become a research hotspot due to its significant public health impact. To explore the value of the use of modified lung ultrasound (MLUS) scoring system for evaluating ALI using a rabbit model of ALI induced by hydrochloric acid (HCl) and investigate its correlation with high-resolution computed tomography (HRCT) and histopathological scores. METHODS: Twenty New Zealand laboratory rabbits were randomly assigned to control group (N = 5) and 3 experimental groups (N = 5 each). The control group received instillation of physiological saline, while the 3 experimental groups received 2 mL/kg of different doses of HCl instillation (mild group: pH 1.5, moderate group: pH 1.2, and severe group: pH 1.0) through the trachea under ultrasound guidance. Pulmonary ultrasound (using Mindray Reason9 linear array probes with frequency of 6-15 mHz) and HRCT examinations were performed before modeling (0H) and at 1H, 2H, 4H, 8H, 12H after modeling. The experimental rabbits were sacrificed at 12H for examination of gross lung morphology and hematoxylin-eosin-stained histopathological sections. The correlation of MLUS scores with HRCT/histopathological scores was assessed. RESULTS: All rabbits in the experimental groups showed oxygenation index PaO2/FiO2<300. Successful establishment of ALI model was proven by autopsy (successful modeling rate: 100%). The pathological damage increased with increase in HCl dosage. MLUS scores showed a positive correlation with HRCT scores/pathological severity. There was a strong positive correlation between MLUS scores and histopathological scores (r = 0.963, p < 0.05) as well as between HRCT scores and histopathological scores (r = 0.932, p < 0.05). CONCLUSION: Transtracheal injection of different dosages of HCl under ultrasound guidance induced different degrees of ALI. The MLUS scoring system can be used for semiquantitative evaluation of ALI, and is suitable as a screening tool.

Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome.

BACKGROUND: Neonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease. METHODS: A total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children's Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise. RESULTS: Twenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001). CONCLUSION: The RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.

Lung Ultrasound Assessment of Lung Hyperinflation in Patients with Stable COPD: An Effective Diagnostic Tool.

Purpose: To evaluate the degree of lung hyperinflation (LH) in patients with stable chronic obstructive pulmonary disease (COPD) by lung ultrasound score (LUS) and assess its value. Patients and Methods: We conducted a study of 149 patients with stable COPD and 100 healthy controls recruited by the Second Affiliated Hospital of Fujian Medical University. The pleural sliding displacement (PSD) was measured, the sliding of the pleura in different areas was observed, and LUS was calculated from both of them. The diaphragm excursion (DE), residual capacity (RV), total lung capacity (TLC), inspiratory capacity (IC) and functional residual capacity (FRC) were measured. We described the correlation between ultrasound indicators and pulmonary function indicators reflecting LH. Multiple linear regression analysis was used. The ROC curves of LUS and DE were drawn to evaluate their diagnostic efficacy, and De Long method was used for comparison. Results: (1) The LUS of patients with stable COPD were positively correlated with RV, TLC, RV/TLC and FRC and negatively correlated with IC and IC/TLC (r1=0.72, r2=0.41, r3=0.72, r4=0.70, r5=-0.56, r6=-0.65, P < 0.001). The correlation was stronger than that between DE at maximal deep inspiration and the corresponding pulmonary function indices (r1=-0.41, r2=-0.26, r3=-0.40, r4=-0.43, r5=0.30, r6=0.37, P < 0.001). (2) Multiple linear regression analysis showed that LUS were significantly correlated with IC/TLC and RV/TLC. (3) With IC/TLC<25% and RV/TLC>60% as the diagnostic criterion of severe LH, the areas under the ROC curves of LUS and DE at maximal deep inspiration for diagnosing severe LH were 0.914 and 0.385, 0.845 and 0.543, respectively (P < 0.001). Conclusion: The lung ultrasound score is an important parameter for evaluating LH. LUS is better than DE at maximal deep inspiration for diagnosing severe LH and is expected to become an effective auxiliary tool for evaluating LH.

SEG-LUS: A novel ultrasound segmentation method for liver and its accessory structures based on muti-head self-attention.

Although liver ultrasound (US) is quick and convenient, it presents challenges due to patient variations. Previous research has predominantly focused on computer-aided diagnosis (CAD), particularly for disease analysis. However, characterizing liver US images is complex due to structural diversity and a limited number of samples. Normal liver US images are crucial, especially for standard section diagnosis. This study explicitly addresses Liver US standard sections (LUSS) and involves detailed labeling of eight anatomical structures. We propose SEG-LUS, a US image segmentation model for the liver and its accessory structures. In SEG-LUS, we have adopted the shifted windows feature encoder combined with the cross-attention mechanism to adapt to capturing image information at different scales and resolutions and address context mismatch and sample imbalance in the segmentation task. By introducing the UUF module, we achieve the perfect fusion of shallow and deep information, making the information retained by the network in the feature extraction process more comprehensive. We have improved the Focal Loss to tackle the imbalance of pixel-level distribution. The results show that the SEG-LUS model exhibits significant performance improvement, with mPA, mDice, mIOU, and mASD reaching 85.05%, 82.60%, 74.92%, and 0.31, respectively. Compared with seven state-of-the-art semantic segmentation methods, the mPA improves by 5.32%. SEG-LUS is positioned to serve as a crucial reference for research in computer-aided modeling using liver US images, thereby advancing the field of US medicine research.

Evaluation of extravascular lung water and cardiac function in normal vaginal delivery by intrapartum bedside ultrasound.

BACKGROUND: Healthy parturients may experience pulmonary edema and disturbed cardiac function during labor. We aimed to evaluate the extravascular lung water (EVLW), intravascular volume, and cardiac function of normal parturients during spontaneous vaginal delivery by bedside ultrasound. And to explore the correlation between EVLW and intravascular volume, cardiac function. METHODS: This was a prospective observational study including 30 singleton-term pregnant women undergoing spontaneous vaginal delivery. Bedside ultrasound was performed at the early labor, the end of the second stage of labor, 2 and 24 h postpartum, and 120 scanning results were recorded. EVLW was evaluated by the echo comet score (ECS) obtained by the 28-rib interspaces technique. Inferior vena cava collapsibility index (IVC-CI), left ventricle ejection fraction, right ventricle fractional area change, left and right ventricular E/A ratio, and left and right ventricular index of myocardial performance (LIMP and RIMP) were measured. Measurements among different time points were compared, and the correlations between ECS and other measurements were analyzed. RESULTS: During the spontaneous vaginal delivery of healthy pregnant women, 2 had a mild EVLW increase at the early labor, 8 at the end of the second stage of labor, 13 at 2 h postpartum, and 4 at 24 h postpartum (P < 0.001). From the early labor to 24 h postpartum, ECS first increased and then decreased, reaching its peak at 2 h postpartum (P < 0.001). IVC-CI first decreased and then increased, reaching its minimum at the end of the second stage of labor (P < 0.001). RIMP exceeded the cut-off value of 0.43 at the end of the second stage of labor. ECS was weakly correlated with IVC-CI (r=-0.373, P < 0.001), LIMP (r = 0.298, P = 0.022) and RIMP (r = 0.211, P = 0.021). CONCLUSIONS: During spontaneous vaginal delivery, the most vital period of perinatal care is between the end of the second stage of labor and 2 h postpartum, because the risk of pulmonary edema is higher and the right ventricle function may decline. IVC-CI can be used to evaluate maternal intravascular volume. The increase in EVLW may be related to the increase in intravascular volume and the decrease in ventricular function.

Nomogram model for predicting invasive placenta in patients with placenta previa: integrating MRI findings and clinical characteristics.

This study aims to validate a nomogram model that predicts invasive placenta in patients with placenta previa, utilizing MRI findings and clinical characteristics. A retrospective analysis was conducted on a training cohort of 269 patients from the Second Affiliated Hospital of Fujian Medical University and a validation cohort of 41 patients from Quanzhou Children's Hospital. Patients were classified into noninvasive and invasive placenta groups based on pathological reports and intraoperative findings. Three clinical characteristics and eight MRI signs were collected and analyzed to identify risk factors and develop the nomogram model. The mode's performance was evaluated in terms of its discrimination, calibration, and clinical utility. Independent risk factors incorporated into the nomogram included the number of previous cesarean sections ≥ 2 (odds ratio [OR] 3.32; 95% confidence interval [CI] 1.28-8.59), type-II placental bulge (OR 17.54; 95% CI 3.53-87.17), placenta covering the scar (OR 2.92; CI 1.23-6.96), and placental protrusion sign (OR 4.01; CI 1.06-15.18). The area under the curve (AUC) was 0.908 for the training cohort and 0.803 for external validation. The study successfully developed a highly accurate nomogram model for predicting invasive placenta in placenta previa cases, based on MRI signs and clinical characteristics.

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).

Artificial intelligence (AI) has emerged as a crucial technique for extracting high­throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI­based radiomics has proven to be a non­invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI­based multi­omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

FHUSP-NET: A Multi-task model for fetal heart ultrasound standard plane recognition and key anatomical structures detection.

In prenatal ultrasound screening, rapid and accurate recognition of the fetal heart ultrasound standard planes(FHUSPs) can more objectively predict fetal heart growth. However, the small size and movement of the fetal heart make this process more difficult. Therefore, we design a deep learning-based FHUSP recognition network (FHUSP-NET), which can automatically recognize the five FHUSPs and detect tiny key anatomical structures at the same time. 3360 ultrasound images of five FHUSPs from 1300 mid-pregnancy pregnant women are included in this study. 10 fetal heart key anatomical structures are manually annotated by experts. We apply spatial pyramid pooling with a fully connected spatial pyramid convolution module to capture information about targets and scenes of different sizes as well as improve the perceptual ability and feature representation of the model. Additionally, we adopt the squeeze-and-excitation networks to improve the sensitivity of the model to the channel features. We also introduce a new loss function, the efficient IOU loss, which makes the model effective for optimizing similarity. The results demonstrate the superiority of FHUSP-NET in detecting fetal heart key anatomical structures and recognizing FHUSPs. In the detection task, the value of mAP@0.5, precision, and recall are 0.955, 0.958, and 0.931, respectively, while the accuracy reaches 0.964 in the recognition task. Furthermore, it takes only 13.6 ms to detect and recognize one FHUSP image. This method helps to improve ultrasonographers' quality control of the fetal heart ultrasound standard plane and aids in the identification of fetal heart structures in a less experienced group of physicians.

The application value of lung ultrasound scoring in assessing disease severity: Evaluation of small-scale outbreaks of COVID-19.

PURPOSE: This study explored the use of transthoracic lung ultrasound for evaluating COVID-19 patients, compared it with computed tomography (CT), and examined its effectiveness using 8 and 12 lung regions. METHODS: A total of 100 patients with COVID-19 and 40 healthy volunteers were assessed using 12 regions (bilateral upper/lower regions of the anterior/lateral/posterior chest) and simplified 8 zones (bilateral upper/lower regions of the anterior/lateral chest) transthoracic lung ultrasound. The relationships between ultrasound, CT, and clinical indicators were analyzed to evaluate the diagnostic value of ultrasound scores in COVID-19. RESULTS: Increased disease severity correlated with increased 8- and 12-zone ultrasound and CT scores (all p < 0.05). The modified 8-zone method strongly correlated with the 12-zone method (Pearson's r = 0.908, p < 0.05). The 8- and 12-zone methods correlated with CT scoring (correlation = 0.568 and 0.635, respectively; p < 0.05). The intragroup correlation coefficients of the 8-zone, 12-zone, and CT scoring methods were highly consistent (intragroup correlation coefficient = 0.718, p < 0.01). The 8-zone ultrasound score correlated negatively with oxygen saturation (rs = 0.306, p < 0.05) and Ca (rs = 0.224, p < 0.05) and positively with IL-6 (rs = 0.0.335, p < 0.05), erythrocyte sedimentation rate (rs = 0.327, p < 0.05), alanine aminotransferase (rs = 0.230, p < 0.05), and aspartate aminotransferase (rs = 0.251, p < 0.05). The 12-zone scoring method correlated negatively with oxygen saturation (rs = 0.338, p < 0.05) and Ca (rs = 0.245, p < 0.05) and positively with IL-6 (rs = 0.354, p < 0.05) and erythrocyte sedimentation rate (rs = 0.495, p < 0.05). CONCLUSION: Lung ultrasound scores represent the clinical severity and have high clinical value for diagnosing COVID-19 pneumonia. The 8-zone scoring method can improve examination efficiency and reduce secondary injuries caused by patient movement.

Latest progress in low-intensity pulsed ultrasound for studying exosomes derived from stem/progenitor cells.

Stem cells have self-renewal, replication, and multidirectional differentiation potential, while progenitor cells are undifferentiated, pluripotent or specialized stem cells. Stem/progenitor cells secrete various factors, such as cytokines, exosomes, non-coding RNAs, and proteins, and have a wide range of applications in regenerative medicine. However, therapies based on stem cells and their secreted exosomes present limitations, such as insufficient source materials, mature differentiation, and low transplantation success rates, and methods addressing these problems are urgently required. Ultrasound is gaining increasing attention as an emerging technology. Low-intensity pulsed ultrasound (LIPUS) has mechanical, thermal, and cavitation effects and produces vibrational stimuli that can lead to a series of biochemical changes in organs, tissues, and cells, such as the release of extracellular bodies, cytokines, and other signals. These changes can alter the cellular microenvironment and affect biological behaviors, such as cell differentiation and proliferation. Here, we discuss the effects of LIPUS on the biological functions of stem/progenitor cells, exosomes, and non-coding RNAs, alterations involved in related pathways, various emerging applications, and future perspectives. We review the roles and mechanisms of LIPUS in stem/progenitor cells and exosomes with the aim of providing a deeper understanding of LIPUS and promoting research and development in this field.

Effects and mechanisms of intrauterine chronic hypoxia on ovarian reserve function of zygotic rats.

Chronic intrauterine hypoxia (ICH) may lead to permanent alterations in the offspring's body structure, function, and metabolism through the "developmental programming" pathway, resulting in lasting changes in physiology and metabolism, as well as the onset of adult-onset diseases. The aim was to investigate intrauterine growth restriction caused by ICH and its effect on ovarian reserve function in female offspring at different developmental stages after birth. Healthy female Sprague-Dawley rats (n = 20) were pregnant by normal mating, and the rats in the ICH group were treated with chronic intrauterine hypoxia twice a day for 04 h00 each time from day 4 to 21 of gestation. After the first hypoxic treatment, four pregnant rats were randomly selected from the ICH and natural control groups for arterial blood gas analysis. In the ICH group, birth weight and body weight on the 5th day after birth were less than in the control group, the total number of follicles and the number of primordial follicles in the offspring of the ICH group were significantly reduced on postnatal days 5, 20, and 40 (p < 0.05). ICH decreases ovarian reserve function in female offspring rats and programmatically regulates the differential expression of ovarian miRNAs in female offspring rats.

Risk estimation for postoperative nausea and vomiting: development and validation of a nomogram based on point-of-care gastric ultrasound.

BACKGROUND: We aimed to develop a nomogram that can be combined with point-of-care gastric ultrasound and utilised to predict postoperative nausea and vomiting (PONV) in adult patients after emergency surgery. METHODS: Imaging and clinical data of 236 adult patients undergoing emergency surgery in a university hospital between April 2022 and February 2023 were prospectively collected. Patients were divided into a training cohort (n = 177) and a verification cohort (n = 59) in a ratio of 3:1, according to a random number table. After univariate analysis and multivariate logistic regression analysis of the training cohort, independent risk factors for PONV were screened to develop the nomogram model. The receiver operating characteristic curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the prediction efficiency, accuracy, and clinical practicability of the model. RESULTS: Univariate analysis and multivariate logistic regression analysis showed that female sex, history of PONV, history of migraine and gastric cross-sectional area were independent risk factors for PONV. These four independent risk factors were utilised to construct the nomogram model, which achieved significant concordance indices of 0.832 (95% confidence interval [CI], 0.771-0.893) and 0.827 (95% CI, 0.722-0.932) for predicting PONV in the training and validation cohorts, respectively. The nomogram also had well-fitted calibration curves. DCA and CIC indicated that the nomogram had great clinical practicability. CONCLUSIONS: This study demonstrated the prediction efficacy, differentiation, and clinical practicability of a nomogram for predicting PONV. This nomogram may serve as an intuitive and visual guide for rapid risk assessment in patients with PONV before emergency surgery.

Performance of two-dimensional shear wave elastography for detecting advanced liver fibrosis and cirrhosis in patients with biliary atresia: a systematic review and meta-analysis.

BACKGROUND: Two-dimensional shear wave elastography (2D-SWE) has been proposed for detecting liver fibrosis in biliary atresia. OBJECTIVES: To assess the performance of 2D-SWE for detecting advanced liver fibrosis and cirrhosis in patients with biliary atresia. MATERIALS AND METHODS: Five electronic databases were searched to identify studies investigating the performance of 2D-SWE for diagnosing liver fibrosis in biliary atresia in children. We constructed the summary receiver operating characteristic (SROC) curves of 2D-SWE for detecting advanced liver fibrosis and cirrhosis, and then calculated the area under the SROC curves (AUROCs). RESULTS: Six studies with 470 patients (ages 55 days to 6.6 years) were included. The median correlation coefficient of 2D-SWE with pathological liver fibrosis stages was 0.779 (range: 0.443‒0.813). The summary AUROCs for advanced liver fibrosis and cirrhosis were 0.929 and 0.883, respectively. The summary sensitivity and specificity of 2D-SWE for advanced liver fibrosis were 88% (95% confidence interval [CI]: 80‒94%) and 85% (95% CI: 77‒91%) with I values of 0% and 45.6%, respectively, and for cirrhosis were 80% (95% CI: 72‒87%) and 82% (95% CI: 77‒86%) with I values of 12.9% and 0%, respectively. The diagnostic odds ratio (DOR) of 2D-SWE for advanced liver fibrosis and cirrhosis were 40.3 (95% CI: 18.2‒89.4) and 18.9 (95% CI: 11.2‒31.7), respectively. For preoperative detection of cirrhosis, the pooled AUROC, sensitivity, specificity, and DOR based on the four 2D-SWE studies were 0.877, 79% (95% CI: 71‒86%), 82% (95% CI: 77‒86%), and 17.58 (95% CI: 10.35‒29.85), respectively. CONCLUSIONS: Results show that 2D-SWE has potential as a non-invasive tool for detecting advanced liver fibrosis and cirrhosis in patients with biliary atresia.

The Right Ventricular Fetal Tricuspid Annular Plane Systolic Excursion Index Is a New Index for Evaluating Fetal Cardiac Function of Gestational Hypertension.

ABSTRACT: The right ventricular fetal tricuspid annular plane systolic excursion index (FTI) can be used to evaluate right ventricular systolic function. The purpose of this study was to establish the reference range of the FTI in normal fetuses and evaluate its diagnostic value in hypertensive disorders during pregnancy. In this prospective observational study, the right ventricular FTI was measured in 208 normal single-gestation fetuses between 20 and 40 weeks. With the increase in gestational age, the right ventricular FTI did not significantly fluctuate. With the increase in the severity of HDCP, the right ventricular FTI decreased gradually. Compared with the normal group, the low right ventricular FTI group had a higher incidence of premature delivery and emergency delivery due to continuous abnormal fetal heart monitoring, but there were no significant differences in low birth weight, new born Apgar score less than 7 in 5 minutes, or admission to the neonatal intensive care unit. The FTI of the right ventricle of normal fetuses is relatively constant at different gestational weeks. The right ventricular FTI can be used to evaluate fetal cardiac function changes in pregnant women with HDCP.

Detection of fetal facial anatomy in standard ultrasonographic sections based on real-time target detection network.

At present, prenatal ultrasound is one of the important means for screening fetal malformations. In the process of prenatal ultrasound diagnosis, the accurate recognition of fetal facial ultrasound standard plane is crucial for facial malformation detection and disease screening. Due to the dense distribution of fetal facial images, no obvious structure contour boundary, small structure area, and large area overlap in the middle of the structure detection frame, this paper regards the fetal facial standard plane and its structure recognition as a universal target detection task for the first time, and applies real-time YOLO v5s to the fetal facial ultrasound standard plane structure detection and classification task. First, we detect the structure of a single slice, and take the structure of a slice class as the recognition object. Second, we carry out structural detection experiments on three standard planes; then, on the basis of the previous stage, the images of all parts included in the ultrasound examination of multiple fetuses were collected. In the single-class structure detection experiment and the structure detection and classification experiment of three types of standard planes, the overall recognition effect of Precision and Recall index data is better, with Precision being 98.3% and 98.1%, and Recall being 99.3% and 98.2%, respectively. The experimental results show that the model has the ability to identify fetal facial anatomy and standard sections in different data, which can help the physician to automatically and quickly screen out the standard sections of each fetal facial ultrasound.

A nomogram for predicting the risk of intra-abdominal hypertension in critically ill patients based on ultrasound and clinical data.

Background: Intra-abdominal hypertension (IAH) is a common complication in critically ill patients. This study aimed to identify independent risk factors for IAH and generate a nomogram to distinguish IAH from non-IAH in these patients. Methods: We retrospectively analyzed 89 critically ill patients and divided them into an IAH group [intra-abdominal pressure (IAP) ≥12 mmHg] and a non-IAH group (IAP <12 mmHg) based on the IAP measured from their bladders. Ultrasound and clinical data were also measured. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for IAH. The correlation between IAP and independent risk factors was also assessed. Results: Of these 89 patients, 45 (51%) were diagnosed with IAH. Univariate analysis showed there were significant differences in the right renal resistance index (RRRI) of the interlobar artery, the right diaphragm thickening rate (RDTR), and lactic acid (Lac) between IAH and non-IAH groups (P<0.001). Multivariate logistic regression analysis revealed that increasing RRRI, RDTR, and Lactic acid (Lac) were independent risk factors for IAH (P=0.001, P=0.001, and P=0.039, respectively). IAP was significantly correlated with RRRI, RDTR, and Lac (r=0.741, r=-0.774, and r=0.396, respectively; P<0.001). The prediction model based on regression analysis results was expressed as follows: predictive score = -17.274 + 31.125 × RRRI - 29.074 × RDTR + 0.621 × Lac. Meanwhile, the IAH nomogram prediction model was established with an area under the receiver operating characteristic (ROC) curve of 0.956 (95% confidence interval: 0.909-1.000). The nomogram showed good calibration for IAH with the Hosmer-Lemeshow test (P=0.864) and was found to be applicable within a wide threshold probability range, especially that higher than 0.40. Conclusions: The noninvasive nomogram based on ultrasound and clinical data has good diagnostic efficiency and can predict the risk of IAH. This nomogram may provide valuable guidance for clinical interventions to reduce IAH morbidity and mortality in critically ill patients.

Ultrasound Detection of Fetal Palate Development in the Early Stages of the Second Trimester and Its Clinical Application.

OBJECTIVE: To establish normal values of palatal bone growth in fetuses at different gestational weeks in the early stages of the second trimester and to explore the clinical application value of the four-step ultrasound screening method for fetal cleft lip and palate. DESIGN: A prospective study of prenatal ultrasound screening. SETTING: Secondary maternal and child health institutions. PATIENTS: 300 fetuses of 12 to 20 +6 weeks gestation without cleft lip and/or palate; 8538 fetuses at high risk of cleft lip and palate with malformations or karyotypic abnormalities. INTERVENTIONS: None. MAIN OUTCOME MEASURES: palatomandibular diameter (PMD) and transverse palatal diameter was measured and establish their typical values. RESULTS: (1) There is a typical "superimposed line" sign in the median sagittal section of the typically developing fetal face from 12 to 20+6 weeks of gestation. (2) The PMD and hard palate transverse diameter of fetuses from 12 to 20+6 weeks of gestation increased linearly with time. (3) Among 8538 high-risk fetuses, 21 cases of cleft lip and palate were diagnosed by the four-step ultrasound screening method in the early stages of the second trimester. CONCLUSIONS: The median sagittal section of the typically developing fetal face in the early stages of the second trimester presents a typical "superimposed line" sign, and the PMD and transverse palatal diameter increase with time. The four-step ultrasound screening method for fetal cleft lip and palate in the early stages of the second trimester has high clinical application value.