View clinical trials related to Ultrasound.
Filter by:Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. The investigators have already developed a deep convolutional network (DCNN) model that automates detailed classification of ATFL injuries. The investigators hope to use the DCNN in real-world clinical setting to test its diagnostic accuracy.
Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. Using datasets from multiple clinical centers, the investigators aimed to develop and validate a deep convolutional network (DCNN) model that automates classification of ATFL injuries using US images with the goal of providing interpretable assistance to radiologists and facilitating a more accurate diagnosis of ATFL injuries. The investigators collected US images of ATFL injuries which had arthroscopic surgery results as reference standard form 13 hospitals across China;Then the investigators divided the images into training dataset, internal validation dataset, and external validation dataset in a ratio of 8:1:1; the investigators chose an optimal DCNN model to test its diagnostic performance of the model, including the diagnostic accuracy, sensitivity, specificity, F1 score. At last, the investigators compared the diagnostic performance of the model with 12 radiologists at different levels of expertise.
During pregnancy, certain conditions may arise that mean regular monitoring of both mother and baby are needed to ensure timely interventions and avoid the need for further treatments. These situations include problems with high blood pressure, obstetric cholestasis (characterised by liver-related itchiness), preterm premature rupture of membranes (PPROM), and a history of stillbirth. Monitoring typically involves assessing the mother's blood pressure and urine, recording the baby's heart rate over a specific duration, and conducting regular ultrasound scans. Such monitoring can require frequent hospital visits, often multiple times a week, which can be very time consuming. More recently, new technology has emerged, enabling remote monitoring of mother and baby outside of the hospital setting, such as their own home. However, research on these technologies is still very limited. Our study aims to address this research gap by inviting women with the above conditions to volunteer for home-based monitoring, alongside their routine hospital care. Participants will be divided into three groups: one group will use transducers, attached to the mothers tummy, to capture the baby's heartbeat; another group will use a handheld ultrasound device connected to their mobile phones, allowing them to observe the baby; and a third group will use both devices. All device information will be transmitted securely to the healthcare professional for analysis. The investigators aim to assess the feasibility of conducting remote monitoring of mother and baby, whilst understanding how acceptable the technology is received. Importantly, the data collected will only be evaluated by the research team and will not be intended to influence patient's current planned antenatal care. Women will receive comprehensive training on the devices. The study will additionally gather feedback from participating women through questionnaires, both at the study's outset and its conclusion, regarding their experiences and emotions related to the research.
The aim of the study is to investigate if hands-on training for basic CCE with virtual reality simulators or guided by artificial intelligence is non-inferior to training by an experienced instructor.
The goal of this observational study is to quantitatively assess the renal microcirculation changes by contrast-enhanced ultrasound (CEUS) and to obtain systemic hemodynamic information by ultrasound Doppler at the same time, to analyze the relationship between renal microcirculation changes and systemic hemodynamic changes, and to explore the diagnostic value of CEUS in critically ill acute kidney injury.
The goal of this study is to quantitatively assess renal microcirculation changes by contrast-enhanced ultrasound and to obtain systemic hemodynamic information by ultrasound Doppler at the same time, to analyze the relationship between renal microcirculation changes and systemic hemodynamic changes, and to explore its predictive value in renal function recovery in patients with critical acute kidney injury. The main questions it aims to answer are: 1. To explore the quantitative parameters of contrast-enhanced ultrasound which can reflect the changes of renal microcirculation. 2. To explore the relationship between renal microcirculation and systemic hemodynamics. 3. To explore the value of renal microflow changes quantitatively evaluated by contrast-enhanced ultrasound in predicting renal function recovery.
1. To explore the diagnostic value of musculoskeletal cross-modal imaging assessment system of ultrasound combined with abdominal CT/MRI for sarcopenia in patients with lung cancer. 2. To explore the value of musculoskeletal cross-modal imaging assessment system of ultrasound combined with abdominal CT/MRI in evaluating the prognosis and the effect of nutritional support in patients with lung cancer during perioperative period. 3. To explore the value of musculoskeletal cross-modal imaging assessment system of ultrasound combined with abdominal CT/MRI in evaluating the long-term prognosis of patients with lung cancer.
Postextubation distress is detrimental to the prognosis of critically ill patients with successful spontaneous breathing trial. Failure to wean is known to be connected with heart, lung, and diaphragm problems. The aim of this study was to investigate how a composite model comprising diaphragm, lung, and heart ultrasonography indications could predict the weaning outcome. Methods: Ultrasonic features of the diaphragm, heart, and lungs are going to be collected along with clinical data about the patients. Either the successful weaning group or the failed weaning group is going to comprised the patients. Multivariate logistic regression analysis is going to be used to identify the variables that may be associated with the likelihood of weaning failure. A multiindicator combination model is going to be developed to increase the predictive accuracy after the possible indicators' accuracy in foretelling the weaning outcome was assessed.
The SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI.
1. To develop an artificial intelligence assisted diagnostic model for sarcopenia based on ultrasound images; 2. To develop artificial intelligence classification and regression models for auxiliary diagnosis of sarcopenia, patient strength estimation, and other functions based on ultrasound image data.