Scientists have also discovered a way to use artificial intelligence to predict the risk of a heart attack or stroke using only a single x-ray. Cardiovascular disease is the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are estimated to take 17.9 million lives each year. The devastating consequences of this disease have inspired researchers to work toward the cure and prevention of heart disease and risk factors. Using a single chest X-ray, the researchers have now developed a learning model that predicts the 10-year probability of dying from a heart attack or stroke caused by atherosclerotic cardiovascular disease.

Using a single X-ray, this AI can predict the risk of a heart attack or stroke. Cardiovascular disease is the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) are estimated to take 17.9 million lives each year. The devastating consequences of this disease have inspired researchers to work toward the cure and prevention of heart disease and risk factors. Using a single chest X-ray, the researchers have now developed a learning model that predicts the 10-year probability of dying from a heart attack or stroke caused by atherosclerotic cardiovascular disease.

The first step in choosing the appropriate treatment for a cancer patient is to identify their specific type of cancer, including determining the primary site -- the organ or part of the body where the cancer begins. In rare cases, the origin of a cancer cannot be determined, even with extensive testing. Although these cancers of unknown primary tend to be aggressive, oncologists must treat them with non-targeted therapies, which frequently have harsh toxicities and result in low rates of survival. A new deep-learning approach developed by researchers at the Koch Institute for Integrative Cancer Research at MIT and Massachusetts General Hospital (MGH) may help classify cancers of unknown primary by taking a closer look the gene expression programs related to early cell development and differentiation. "Sometimes you can apply all the tools that pathologists have to offer, and you are still left without an answer," says Salil Garg, a Charles W. (1955) and Jennifer C. Johnson Clinical Investigator at the Koch Institute and a pathologist at MGH. "Machine learning tools like this one could empower oncologists to choose more effective treatments and give more guidance to their patients."

The new UCF co-developed algorithm can accurately identify COVID-19 cases, as well as distinguish them from influenza. ORLANDO, Sept. 30, 2020 - A University of Central Florida researcher is part of a new study showing that artificial intelligence can be nearly as accurate as a physician in diagnosing COVID-19 in the lungs. The study, recently published in Nature Communications, shows the new technique can also overcome some of the challenges of current testing. Researchers demonstrated that an AI algorithm could be trained to classify COVID-19 pneumonia in computed tomography (CT) scans with up to 90 percent accuracy, as well as correctly identify positive cases 84 percent of the time and negative cases 93 percent of the time. CT scans offer a deeper insight into COVID-19 diagnosis and progression as compared to the often-used reverse transcription-polymerase chain reaction, or RT-PCR, tests.

Accurate simulation of cancer-implicated proteins holds enormous promise for basic biomedical science and development of effective therapies, but the high computational cost required has long slowed progress. Recently a multi-institution research team developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Their work on simulating the RAS protein family will be published at SC19 and is a finalist for the Best Paper award. RAS proteins are implicated in roughly one third of cancers, and research to obtain a more detailed understanding of how they interact with the cell's lipid membranes and influence signaling pathways has long been pursued. One way to shortcut the simulations needed and to reduce the computational cost is to use ML to zoom in on areas of interest.

Artificial intelligence (AI) is everywhere: personal digital assistants answer our questions, robo-advisors trade stocks for us, and driverless cars will someday take us where we want to go. AI has penetrated our lives, and its use is exploding in biomedical research and health care--including across all dimensions of cancer research, where the potential applications for AI are vast. Artificial Intelligence (AI) is a computer performing tasks commonly associated with human intelligence. Humans are coding or programing a computer to act, reason, and learn. An algorithm or model is the code that tells the computer how to act, reason, and learn.

Researchers from the National Institutes of Health (NIH) and Global Good have created a computer algorithm capable of identifying precancerous changes in women which place them at risk of developing cervical cancer. Known as automated visual evaluation, this new form of artificial intelligence (AI), "has the potential to revolutionize cervical cancer screening" for women in low income communities worldwide by giving their healthcare providers the ability to use digitized images collected during routine, annual screenings for cervical cancer to identify potential precancerous changes. According to America's National Cancer Institute (which is part of the NIH), this technology holds the promise of enabling physicians to more quickly catch and treat such potential changes before they develop into cancer, and could eventually replace visual inspection with acetic acid (VIA) -- the current method of screening used by healthcare professionals who work with limited resources in challenging medical care environments -- a testing system which is "known to be inaccurate." The researchers involved in this project "trained" the machine learning algorithm (automated visual evaluation) to recognize patterns in medical images and other "complex visual inputs" by digitizing and entering more than 60,000 images from an NCI archive of photographs which had been collected from more than 9,400 women in Costa Rica during a 1990s cervical cancer screening study which included follow-up studies for roughly 18 years. These images subsequently enabled the algorithm to "learn" which "cervical changes became precancers and which did not," according to NIH representatives, who added that the AI approach to cervical cancer screening was developed by NCI researchers in collaboration with the Intellectual Ventures Fund, Global Good, with findings confirmed independently by personnel from the National Library of Medicine (NLM), another component of the NIH.

A research collaboration between the National Cancer Institute (NCI) and Lawrence Livermore National Laboratory (LLNL) is demonstrating the value of using machine learning to overcome daunting computational challenges. Although the specific goal of NCI-LLNL work is to advance the understanding of the biomolecular mechanisms that underly some of the most aggressive human cancers, the computational approach that was employed to do this has more far-reaching application. At least that's the claim of Fred Streitz, LLNL's Chief Computational Scientist and HPC Innovation Center Director, who led the project at the national lab. According to Streitz, the collaboration was very much in the interest of both organizations: NCI reaped the direct benefit of advancing their cancer research work, while LLNL got the opportunity to explore new ways of using machine learning to cut intractable HPC problems down to size. "It turns out that the workflows that are necessary to understand some of these biology problems are different than what we are currently doing," Streitz told The Next Platform.

What role will AI play in cervical cancer screening? Will artificial intelligence, otherwise known as AI, soon be at your cervix to help prevent and detect cancer? A study recently published in the Journal of the National Cancer Institute showed how AI could serve as an extra eye to help doctors find pre-cancerous and cancerous lesions in the part of your body that connects your vagina with your uterus. As I've written previously for Forbes, each year cervical cancer kills around 265,000 women worldwide, but a majority of deaths could be quite preventable. Besides getting the human papilloma virus (HPV) vaccine, the key to preventing cervical cancer is getting regular pelvic exams so that concerning lesions can be caught early.

What role will AI play in cervical cancer screening? Will artificial intelligence, otherwise known as AI, soon be at your cervix to help prevent and detect cancer? A study recently published in the Journal of the National Cancer Institute showed how AI could serve as an extra eye to help doctors find pre-cancerous and cancerous lesions in the part of your body that connects your vagina with your uterus. As I've written previously for Forbes, each year cervical cancer kills around 265,000 women worldwide,but a majority of deaths could be quite preventable. Besides getting the human papilloma virus (HPV) vaccine, the key to preventing cervical cancer is getting regular pelvic exams so that concerning lesions can be caught early.