It is predicted that technologies such as artificial intelligence (AI), cloud computing, extended reality and the Internet of Things (IoT) will be introduced further among related workers, leading to the development and provision of new and better treatments and services. In the months following the outbreak of the COVID-19 outbreak, the proportion of telemedicine consulting has risen sharply from 0.1% to 43.5%, and is expected to rise further in the future, as this trend could save more patients' lives, said Deloitte Accounting Firm analyst. . To achieve this goal, the next-generation portable device, heart rate, stress, and blood oximetry, enables doctors to accurately determine the patient's condition in real time. During the COVID-19 period, doctors built'virtual hospital rooms' in some areas to observe the treatment status of patients in various areas through the central communication infrastructure. The Pennsylvania Emergency Medical Center is developing a high-quality'virtual emergency room'.
North American professional drone maker Draganfly has sent the first of nearly a dozen humanitarian drones to the non-profit Ukraine organization Revived Soldiers Ukraine (RSU) in Europe, to be used to deliver insulin to hard-to-reach hospitals in the war-torn country. RSU has ordered 200 medical response drones from Draganfly, each costing $30,000 and equipped with temperature-managed payload boxes that can transport up to 35 pounds of blood, pharmaceuticals, insulin/medicines, vaccines, and wound care kits, the drone maker said. Because insulin is a temperature-sensitive product, quick and safe transportation is a top priority. There are roughly 2.3 million people living with diabetes in Ukraine, according to the International Diabetes Association, many of whom have Type 1 diabetes and require multiple daily injections of insulin to survive. For those living in high-conflict areas of the country, access to life-saving insulin is limited or non-existent.
A new wearable gadget that fixes to the arm can measure blood sugar and muscle fatigue at the gym and alcohol levels at the pub. Created in California, the prototype can continuously monitor three health stats – glucose, alcohol and lactate levels – either separately or simultaneously in real-time. About the size of three poker chips stacked together, it is applied to the skin painlessly through a Velcro-like patch of microscopic needles. These needles take readings from fluid under the skin and then sends the data wirelessly to a custom smartphone app. Researchers hope to commercialise the device, which could provide a single solution for diabetes patients in everyday life.
Not all data are created equal. But how much information is any piece of data likely to contain? This question is central to medical testing, designing scientific experiments, and even to everyday human learning and thinking. MIT researchers have developed a new way to solve this problem, opening up new applications in medicine, scientific discovery, cognitive science, and artificial intelligence. In theory, the 1948 paper, "A Mathematical Theory of Communication," by the late MIT Professor Emeritus Claude Shannon answered this question definitively.
In this video, Rohin Francis, MBBS, reviews modern health trends and the dangers of unsupported medical claims. The following is a transcript of this video; note that errors are possible. Francis: There is so much that I could say about the collision of the worlds of Silicon Valley and the mindset that drives it, sometimes referred to as the "tech bros," with the world of medicine and the strange bedfellows that they make. I will explore many of the phenomena that arise when this happens in future videos, things like novelty bias, where you assume that something new must be better. While this normally holds true for computing and we're all familiar with Moore's law, it very commonly isn't true in medicine, with many new and exciting therapies being quietly or occasionally loudly shelved years later for being useless or worse, harmful, or how Silicon Valley's motto of "move fast and break things" can be catastrophic for medicine. If you want to hear more about tech and medicine, then please do consider subscribing. But for this video, I want to focus on one aspect, the obsession with data. The belief that if we just measure more and more we can unlock the secrets of the human body. We can use 100% of our brain, become immortal, and transform into supernatural beings comprised of pure energy.
Researchers from the National Institutes of Health Clinical Center developed a new artificial intelligence (AI) model that analyzed various factors relating to pancreas health and fat levels using non-contrast abdominal CT images to detect type 2 diabetes risk. The study, which was published in Radiology, evaluated 8,992 patients, of which 572 had type 2 diabetes mellitus, and 1,880 had dysglycemia. All patient screenings occurred between 2004 and 2016. To build the model researchers used 471 images obtained from various datasets. They divided the photos into three categories: 424 for training, 8 for validation, and 39 for test sets.
Speaking at the 2022 Diabetes UK Professional Conference, Julia Townson (Cardiff University, UK) explained that around a quarter of children with type 1 diabetes in the UK are not diagnosed until they are in diabetic ketoacidosis (DKA), with rates unchanged for 25 years despite public health campaigns, highlighting the need for improved tools for early detection. She said that previous research identified different patterns of primary care contact among children who later go on to develop type 1 diabetes versus those who do not, leading the team to hypothesize that primary care data could be used to flag those likely to be diagnosed with the condition. To investigate this, Townson and colleagues used a machine-learning algorithm drawing on 81 pieces of information from electronic health records studied from 2000 to 2016 to produce a single score that indicates the likelihood of being diagnosed with type 1 diabetes. The information used in the tool included flags such as family history, fatigue, urinary tract infections, obesity, and weight loss, as well as data on the frequency of recent primary care contact relative to average contact frequency for each child. The Welsh SAIL/Brecon registry of approximately 35 million primary care contacts for 1 million children (0.21% with type 1 diabetes) was used as the training dataset, and the tool was tested using the English Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics records involving around 43 million contacts for 1.5 million children (0.10% with type 1 diabetes).
Bethesda (Maryland) [US], April 16 (ANI): A new study has found that a fully-automated artificial intelligence (AI) deep learning model can identify early signs of type 2 diabetes on abdominal CT scans. The findings of the study were published in the journal, 'Radiology'. Type 2 diabetes affects approximately 13 per cent of all U.S. adults and an additional 34.5 per cent of adults meet the criteria for pre-diabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease.
Bethesda (Maryland) [US], April 16 (ANI): A new study has found that a fully-automated artificial intelligence (AI) deep learning model can identify early signs of type 2 diabetes on abdominal CT scans. For this retrospective study, Dr Summers and colleagues, in close collaboration with co-senior author Perry J. Pickhardt, M.D., professor of radiology at the University of Wisconsin School of Medicine & Public Health, used a dataset of patients who had undergone routine colorectal cancer screening with CT at the University of Wisconsin Hospital and Clinics. The deep learning model displayed excellent results, demonstrating virtually no difference compared to manual analysis. In addition to the various pancreatic features, the model also analyzed the visceral fat, density and volumes of the surrounding abdominal muscles and organs. The best predictor's type 2 diabetes in the final model included intrapancreatic fat percentage, pancreas fractal dimension, plaque severity between the L1-L4 vertebra level, average liver CT attenuation, and BMI.