Ultrasound

This research evaluates the use of the echOpen probe in managing patients referred to the Jean Verdier Rapid Diagnosis Unit (UDR) to see how it affects the time to diagnosis. The study focuses on improving diagnosis speed and accuracy and reducing the need for extra examinations. It is divided into three phases, each involving the introduction of the echOpen probe in different care settings, including hospital units, multi-professional health centers, and a Health Bus. The study's intervention involves using the echOpen probe for a systematic 4-point echoscopy examination that covers organs like the liver, spleen, lymph nodes, thyroid, and depending on symptoms, lung, kidney, bladder, and abdomen. The probe is CE marked and will be used alongside standard care. Each phase begins with training doctors to use the probe, followed by patient inclusions and testing the probe's impact on care organization and diagnosis processes. Participants will undergo the echoscopy examination and complete satisfaction questionnaires alongside their physicians. Researchers will measure outcomes such as time to diagnosis, the number of patients with direct access to biopsy, and reduction in additional exams. Patient and physician satisfaction with the echOpen probe will also be assessed. The study includes follow-up visits up to two months after inclusion, with a total inclusion period lasting about 16 months.

Atherosclerotic plaques, which are fatty build-ups in arteries, can develop silently and may rupture, leading to serious cardiovascular events like heart attacks or strokes. Despite treatments and controlling risks such as diabetes and high blood pressure, some people still develop dangerous forms of the disease. Researchers have found that plaques with thin caps, soft centers, or tiny new blood vessels are more likely to rupture, so new imaging methods are being developed to detect these vulnerable plaques earlier. This study evaluates a new 3D ultrasound probe and machine that can capture many more images than standard probes to visualize the entire plaque and its tiny blood vessels more accurately. Patients will receive an infusion of a microbubble contrast agent called SonoVue to enhance imaging. Initial 2D ultrasound images will confirm plaque presence, followed by 3D imaging using a specialized row-column array probe manipulated by a clinician. The 3D scan takes about 2 to 5 minutes and captures multiple images to assess plaque neovascularization. Participants will undergo ultrasound scans of their carotid arteries after receiving the contrast agent via an infusion. Researchers will analyze these images offline to detect new blood vessels within plaques. The main measurement is the detection of carotid plaque neovascularization at baseline. The study begins with consenting patients who have carotid plaques confirmed by previous CEUS scans. Participation involves imaging procedures and consent, with the study lasting as long as these assessments are completed.

Researchers are investigating early detection and risk assessment of hepatocellular carcinoma (HCC) in adults with advanced liver disease, specifically those with liver cirrhosis from various causes. This prospective multicenter study aims to compare ultrasound and abbreviated MRI (AMRI) as surveillance tools to assess their ability to detect HCC, as well as to study how body composition, such as fat and muscle levels, relates to disease progression and HCC risk. Participants with cirrhosis undergo regular clinical evaluations and imaging tests at set intervals, including ultrasound and abbreviated MRI scans. The study collects detailed data on body composition and tracks clinical outcomes like liver-related complications and mortality. These assessments occur over multiple visits, including baseline and follow-ups at 6, 12, and 18 months, with additional monitoring for up to 24 months to observe new cases of HCC and disease progression. During the study, participants receive structured exams, imaging, and body composition measurements at each visit. Researchers evaluate lesion risk for HCC using LI-RADS criteria and measure muscle mass through the Muscle Assessment Score. They also monitor liver stiffness and organ volumes at baseline and follow-up visits. The study's goal is to develop prediction models based on these clinical and imaging data to better understand HCC risk and liver disease progression over time.

Researchers are evaluating a new deep learning model that uses ultrasound video data to estimate blood volume before surgery. This prospective, single-center observational study focuses on adult patients scheduled for surgery, aiming to provide an accurate, non-invasive way to assess blood volume, which is important to prevent complications during surgery. The study is led by Shanghai 6th People's Hospital and addresses the current lack of practical methods for direct blood volume measurement. Participants will undergo preoperative ultrasound scans capturing videos of four major blood vessels: the Internal Jugular Vein, Subclavian Vein, Inferior Vena Cava, and Common Carotid Artery. The true blood volume is calculated using a clinical method involving hemoglobin concentration changes before and after acute normovolemic hemodilution. The collected ultrasound videos will be used to train and validate a deep learning model that combines convolutional and temporal analysis techniques to estimate blood volume from the ultrasound data. During the study, participants will have ultrasound video clips taken shortly before surgery. Researchers will analyze these images alongside blood volume values determined by the clinical method. The main outcome measured is the accuracy of the blood volume estimate within 30 minutes before surgery. The study does not involve treatment changes but focuses on data collection and model validation. Participation lasts through the preoperative period up to surgery, with no additional follow-up specified.

Researchers are investigating a new method to help locate a specific vein on the forearm (the distal cephalic vein) in young children undergoing surgery. This study focuses on children under six years old having elective procedures under general anesthesia. The goal is to find a simple, reliable anatomical landmark to guide the placement of a topical anesthetic patch (EMLA) to ease venipuncture, which can be challenging due to small or hard-to-see veins in young children. Ultrasound is used as a reference standard to validate the landmark's accuracy. The study has two parts: first, ultrasound is used to map and mark the vein's course on the skin during anesthesia, and photographic documentation is collected to develop the landmark. In the second part, anesthesia staff and parents apply the landmark or an EMLA patch guided by a visual instruction, and ultrasound checks if the vein lies beneath the marked or treated area. No additional needle punctures or blood samples are taken beyond routine care. Participants contribute data at one time point during their surgery preparation. Ultrasound measurements include whether the vein is under the marked area, vein depth, diameter, and course. Data are collected securely and pseudonymized. The study involves no added risks, no additional pain, and no biological samples. The main outcome is the success rate of locating the vein within the marked or EMLA-covered area during anesthesia.

This research aims to evaluate Bevonescein, a sterile intravenous drug, in patients undergoing minimally invasive abdominopelvic surgery. The study focuses on assessing the safety, tolerability, and effectiveness of Bevonescein in highlighting nerves and ureters during surgery. It also investigates how the drug behaves in the body and the dose needed to produce clear fluorescent imaging for nerve and ureter visualization. Participants will receive Bevonescein during two study phases: a dose defining phase and a dose expansion phase for each surgical setting. The drug is given as an intravenous infusion, and the study uses specialized imaging systems to record fluorescence signals in targeted tissues. These phases help determine the optimal dose and gather safety and imaging data. Throughout the study, participants will be monitored with fluorescence system surveys approximately 28 days after dosing, plus or minus 5 days. Researchers will collect data on the drug's imaging effects and safety. The total duration includes screening, dosing, and follow-up assessments to evaluate Bevonescein’s performance during minimally invasive surgery.

Carpal tunnel syndrome (CTS) is a common nerve compression disorder affecting the wrist, caused by pressure on the median nerve within the carpal tunnel. Researchers are evaluating the use of ultrasound (US) and nerve conduction studies (NCS) as tools to help diagnose CTS, plan treatment, and monitor patients over time. This study focuses on how ultrasound can assist in understanding nerve changes and anatomical variations related to CTS. Participants will undergo high-resolution, real-time ultrasound imaging of both wrists using a 12 MHz linear array transducer. The ultrasound examinations will take place before surgery and again three months after surgery. This imaging approach aims to measure various nerve parameters such as size, blood flow, and movement, offering a non-invasive and quick method to support diagnosis and follow-up. During the study, patients will receive ultrasound assessments at specified times and clinical evaluations for CTS. Researchers will measure the sensitivity of ultrasound in diagnosing CTS during surgery. The study involves monitoring changes in the median nerve and surrounding structures to help improve diagnosis and management. Participation includes imaging visits and clinical follow-up over a period encompassing pre- and post-surgical assessments.

This research aims to investigate whether artificial intelligence (AI) can detect imaging features typical of Intensive Care Unit-acquired Weakness (ICUAW) using neuromuscular ultrasound. The study focuses on evaluating if AI-based image analysis can identify and monitor ICU patients with ICUAW and whether these AI results correlate with muscle weakness severity, visual muscle echogenicity grading, and 30- and 90-day patient outcomes. ICUAW is a common neuromuscular complication in critically ill patients, often difficult to assess due to patient sedation and limited cooperation during clinical exams. Participants will undergo non-invasive neuromuscular ultrasound of peripheral muscles in the upper and lower limbs. The ultrasound images will be processed using AI, specifically Convolutional Neural Networks, to classify muscle weakness severity. Explainable AI techniques will also be used to highlight the areas within the ultrasound images that contribute to the AI's decisions, helping to understand muscle changes. The study includes groups of critically ill patients with and without ICUAW as well as healthy controls. During the study, researchers will assess muscle echogenicity abnormalities by ultrasound on Day 14 and measure ICUAW severity through various scales. Additional outcomes such as ventilation duration, hospital stay length, survival, frailty, and overall recovery will be evaluated at 30 and 90 days. Data collection involves clinical examinations, scoring systems, and AI image analysis to improve diagnosis and monitoring of muscle weakness in ICU patients.

Researchers are evaluating a new noninvasive system that uses artificial intelligence (AI) to analyze multiple types of imaging, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), to help diagnose and predict outcomes for digestive diseases. This observational study collects and analyzes retrospective imaging, endoscopic, and clinical data from 21 centers in China to build and improve the AI model. The model will then be tested prospectively in two centers and its ability to locate lesions will be checked in real-world endoscopy settings. The study involves using a virtual endoscopy model to assist in diagnosis by integrating and analyzing multimodal imaging features. The AI system is designed to support diagnosis without any invasive procedures. The study will confirm the model's accuracy and effectiveness through retrospective data, prospective validation, and real-world deployment in clinical environments. Participants will contribute data from their imaging and endoscopic exams, with at least one technically adequate CT or MR scan and a high-quality colonoscopy performed within one month of imaging. Researchers will assess the AI model's diagnostic performance by measuring the area under the ROC curve (AUC) over six months. The study includes ongoing monitoring of data quality and imaging accuracy to ensure reliable validation of the AI system.

Researchers are exploring the relationship between ultrasound features, including conventional ultrasound, elastography, and contrast-enhanced ultrasound (CEUS), and pathological prognostic factors in breast cancer. This observational study uses radiomics, a method that extracts detailed imaging information, to better understand these correlations and their potential clinical relevance. The study is sponsored by the Second Affiliated Hospital, School of Medicine, Zhejiang University, and aims to provide insights into breast cancer prognosis. Participants undergo ultrasound imaging assessments, including advanced techniques like elastography and CEUS, alongside standard pathological evaluations. The study observes patients over an average follow-up period of one year to monitor outcomes such as metastasis and death related to breast cancer progression. While endocrine therapy is noted as an intervention, the study primarily focuses on imaging and pathological data rather than treatment comparison. During the study, participants' ultrasound characteristics and pathological findings are recorded and analyzed using radiomics technology. Researchers track disease-free survival and non-disease-free survival times, noting the time to relapse, metastasis, or death due to disease progression. Data collection includes follow-up results and pathology reports to ensure comprehensive monitoring. The total participation duration aligns with the one-year average follow-up to assess primary outcomes accurately.