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The Power Of Artificial Intelligence In The Medical Field - AI Summary

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A strong spasm, or abrupt contraction, of a coronary artery, which can block blood flow to the heart muscle, is a less common reason. As a result of improvements in machine learning and artificial intelligence, it is now possible to detect and diagnose diabetes in its early stages using an automated procedure that is more efficient than manual diagnosis. Based on autonomous comparison with a huge collection of typical fundus photos, the server uses IDx-DR software and a "deep-learning" algorithm to discover retinal abnormalities compatible with DR. One of two outcomes is provided by the software: (1) Refer to an eyecare professional (ECP) if more than moderate DR is discovered; (2) If the results are negative for more than mild DR, rescreen in 12 months. Machine learning algorithms and their ability to synthesize extremely complex data may open up new avenues for tailoring drugs to a person's genetic composition.


Machine Learning Engineer, Platform - Applied AI

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Virta Health is on a mission to transform diabetes care and reverse the type 2 diabetes epidemic. Current treatment approaches aren't working--over half of US adults have either type 2 diabetes or prediabetes. Virta is changing this by helping people reverse type 2 diabetes through innovations in technology, personalized nutrition, and virtual care delivery reinvented from the ground up. We have raised over $350 million from top-tier investors, and partner with the largest health plans, employers, and government organizations to help their employees and members restore their health and live diabetes-free. Join us on our mission to reverse diabetes in 100M people by 2025.


The Myriad Applications Of Ambient Intelligence In Healthcare

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Ambient intelligence is a futuristic concept that makes explicit input and output data collection devices redundant in smart cities. Instead, data capturing and processing tools such as sensors, processors and actuators are embedded in everyday objects encountered by smart city inhabitants. Ambient intelligence, a concept related to pervasive computing, will exist in smart cities to add an added layer of functionality and convenience by adapting to user needs constantly. The embedded sensors and processors will be configured to collect contextual data from users, while AI-based tools will be deployed to draw inferences from the information collected to anticipate their future needs. Ambient intelligence makes pervasive computing more human-centric, a trait that is essential for healthcare.


The Basic Classification of Thyroid Tumors on UltraSound Images using Deep Learning Methods

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Before the training, we need to split dataset. In final, we have 347 images. Pre-processing is the initial stage in refining image data, such as removing distortion, so that it may be utilized to process data more effectively. We apply many methods of pre-processing in this study, including augmentation, which tries to avoid overfitting so that even if the device encounters a difficulty with micro variations, the software can still make accurate predictions. So,The augmentation used is a random rotation distance of 20, random zooming area in the range of 0.2, a random contrast in the range of 0.1, and, a random horizontal flip.


Coronary Artery Disease Prediction

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Heart disease is the leading cause of death. In the US, around 659,000[1] and in Canada 77,000 people die from heart disease each year. The spending on Heart disease costs the United States about $363 billion annually[2] and Canada 22 billion annually. American College of Cardiology and American Heart Association (ACC/AHA) 10-year cardiovascular risk calculator has been challenged for its accuracy by several analyses(Lancet 2013; 382:1762 and JAMA Intern Med 2014; 174:1964). Researchers used data from the MESA(Multi-Ethnic Study of Atherosclerosis) study proved that Framingham-based risk scoring systems and the ACC/AHA calculator risk equation substantially overestimated actual 5-year risk in adults without diabetes, overall and across socio demographic subgroups.[3].


Smart Shoe Developed with AI Treats Diabetic Neuropathy

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Nair's prototype neuromodulation device stimulated nerves with electrical pulses through the skin to improve nerve function. After testing it on himself to ensure the device was safe, Nair asked his mother to try it. "She had given up hope because she had suffered for more than a year, and nothing was working," says Nair. His mother agreed to give his device a try. "She was a little disheartened when she couldn't feel anything the first time she tried it," says Nair. "For the first four weeks, she didn't have any kind of reaction to the neuromodulation stimulus. Then her legs started to respond."


TechCrunch+ roundup: Ahrefs' homepage, digital health trends, 2022 marketing predictions – TechCrunch

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As we get closer to the end of the year, we're running more stories that look back at topics we examined in depth over the last several months, and several that offer well-informed predictions for the year ahead. This week, Bill Taranto, president of Merck's Global Health Innovation Fund, wrote a TechCrunch article that explored six digital health trends his corporate VC fund is tracking as we enter 2022. Between Q1 and Q3 2021, healthcare startups landed $21.3 billion in VC, "dwarfing the previous record of $14.6 billion set in 2020," writes Taranto. "Companies with strong offerings, management teams and balance sheets are poised to capture tremendous value." According to Crunchbase, Merck GHI has made 75 investments with 22 exits so far, including companies that span everything from drug discovery to diabetes detection. Artificial intelligence, IoT and data analytics are the primary drivers of innovation, says Taranto, "especially with data becoming the central currency of healthcare."


Medicine's first autonomous AI could prevent blindness due to diabetes -- if it can reach those most in need

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Once feared as a job-erasing technology devoid of clinical nuance, autonomous artificial intelligence in medicine is growing up. The Food and Drug Administration in 2018 gave its stamp of approval to the first automated AI screening system, an algorithm that can analyze retinal images to detect diabetic retinopathy. And starting Jan. 1, primary care doctors across the country can get paid more reliably for automated screenings of the vision-threatening condition, which ultimately impacts more than half of people with diabetes. "That, in my opinion, is going to be a sort of game-changing moment," said Aaron Lee, an associate professor of ophthalmology at the University of Washington, as a national Medicare reimbursement rate helps more primary care practices decide whether the technology is worth investing in. Unlock this article by subscribing to STAT and enjoy your first 30 days free!


Machine learning and deep learning predictive models for type 2 diabetes: a systematic review - PubMed

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Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability.


Transfer Learning: COVID-19 from Chest X-Rays Classifier

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The Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. (WHO, 2020). While most persons with COVID-19 recover and return to normal health, some patients can have symptoms that can last for weeks or even months after recovery from acute illness.