In the era of Large Language Models (LLMs), embodied artificial intelligence presents transformative opportunities for robotic manipulation tasks. Ultrasound imaging, a widely used and cost-effective medical diagnostic procedure, faces challenges due to the global shortage of professional sonographers. To address this issue, we propose USPilot, an embodied robotic assistant ultrasound system powered by an LLM-based framework to enable autonomous ultrasound acquisition. USPilot is designed to function as a virtual sonographer, capable of responding to patients' ultrasound-related queries and performing ultrasound scans based on user intent. By fine-tuning the LLM, USPilot demonstrates a deep understanding of ultrasound-specific questions and tasks. Furthermore, USPilot incorporates an LLM-enhanced Graph Neural Network (GNN) to manage ultrasound robotic APIs and serve as a task planner. Experimental results show that the LLM-enhanced GNN achieves unprecedented accuracy in task planning on public datasets. Additionally, the system demonstrates significant potential in autonomously understanding and executing ultrasound procedures. These advancements bring us closer to achieving autonomous and potentially unmanned robotic ultrasound systems, addressing critical resource gaps in medical imaging.

Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the patients which is required to be safe. This work presents the preliminary evaluation of a recently introduced control architecture based on the Fractal Impedance Control (FIC) in medical applications. The deployed FIC architecture is robust to delay between the master and the replica robots. It can switch online between an admittance and impedance behaviour, and it is robust to interaction with unstructured environments. Our experiments analyse three scenarios: teleoperated surgery, rehabilitation, and remote ultrasound scan. The experiments did not require any adjustment of the robot tuning, which is essential in medical applications where the operators do not have an engineering background required to tune the controller. Our results show that is possible to teleoperate the robot to cut using a scalpel, do an ultrasound scan, and perform remote occupational therapy. However, our experiments also highlighted the need for a better robots embodiment to precisely control the system in 3D dynamic tasks.

The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a convolutional neural network to classify each frame of an ultrasound video recording. We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. We performed a retrospective experiment on 1457 recordings (comprising 48 million frames) of 20-week ultrasound scans, estimated fetal biometrics in those scans and compared our estimates to the measurements sonographers took during the scan. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals in which the true biometric value is expected to lie.

A stick-on patch that can take an ultrasound scan of a person's insides as they go about their daily life has been hailed as a revolution in medical imaging. The wearable patch, which is the size of a postage stamp, can image blood vessels, the digestive system and internal organs for up to 48 hours, giving doctors a more detailed picture of a patient's health than the snapshots provided by routine scans. In laboratory tests, researchers used the patches to watch people's hearts change shape during exercise, their stomachs expand and shrink as they drank and passed drinks, and their muscles pick up microdamage when weightlifting. Prof Xuanhe Zhao at Massachusetts Institute of Technology, who led the research team, said the patches could "revolutionise" medical imaging because existing scans are very brief, sometimes lasting only seconds, and usually have to be performed in hospitals. Ultimately, Zhao envisions people buying boxes of the patches over the counter and using them, with help from smart algorithms on their mobile phones, to monitor their heart, lungs and digestive systems for early signs of disease or infection, or their muscles during rehabilitation or physical training.

BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture. METHODS: The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet.

Segmentation is robust throughout compression. The venous region is evaluated for full compressibility to rule out DVT. Device: Clarius L7 (2017). The team of researchers aims to diagnose deep vein thrombosis (DVT) as quickly and effectively as traditional radiologist-interpreted diagnostic scans, reduce long patient waiting lists, and avoid patients. Receive medication unnecessarily to treat DVT when they do not have it. DVT is one of the most commonly formed blood clots in the legs, causing swelling, pain, and discomfort.

A team of researchers are developing the use of an artificial intelligence (AI) algorithm with the aim of diagnosing deep vein thrombosis (DVT) more quickly and as effectively as traditional radiologist-interpreted diagnostic scans, potentially cutting down long patient waiting lists and avoiding patients unnecessarily receiving drugs to treat DVT when they don't have it. DVT is a type of blood clot most commonly formed in the leg, causing swelling, pain and discomfort--if left untreated, it can lead to fatal blood clots in the lungs. Researchers at Oxford University, Imperial College and the University of Sheffield collaborated with the tech company ThinkSono (which is led by Fouad Al-Noor and Sven Mischkewitz), to train a machine learning AI algorithm (AutoDVT) to distinguish patients who had DVT from those without DVT. The AI algorithm accurately diagnosed DVT when compared to the gold standard ultrasound scan, and the team worked out that using the algorithm could potentially save health services $150 per examination. "Traditionally, DVT diagnoses need a specialist ultrasound scan performed by a trained radiographer, and we have found that the preliminary data using the AI algorithm coupled to a hand-held ultrasound machine shows promising results," said study lead Dr. Nicola Curry, a researcher at Oxford University's Radcliffe Department of Medicine and clinician at Oxford University Hospitals NHS Foundation Trust.

Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest ( i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector.

Ultrasound scans have the ability to destroy coronavirus cells by forcing their surface to split apart and implode, new research suggests. MIT researchers conducted a mathematical analysis based on the physical properties of generic coronavirus cells. It revealed medical ultrasound scans may be able to damage the virus's shell and spikes, leading to collapse and rupture. Ultrasounds are already used as a treatment for kidney stones but the MIT team are calling for further research on its viability as a treatment for Covid-19. An ultrasound scan, sometimes called a sonogram, is a procedure that uses high-frequency sound waves to create an image of part of the inside of the body.

The companies claim BioAssist and LaborAssist can deliver a better understanding of a patient's birthing progress by automatically taking metrics like fetal angle of progression during labor. According to the World Health Organization, about 295,000 women died during pregnancy and following childbirth in 2017. Research from the Perinatal Institute shows that tracking fetal growth can help prevent stillbirths, as it allows physicians to recognize growth restrictions. But there's little standardization around fetal measurement because doctors often disagree on where to start. Intel and Samsung Medison say BioAssist and LaborAssist perform measurements in as little as 85 microseconds, courtesy of Intel's machine learning toolkit OpenVino.