The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism. RSNA collaborated with the Society of Thoracic Radiology (STR) to create a massive dataset for the challenge. The RSNA-STR Pulmonary Embolism CT (RSPECT) dataset is comprised of more than 12,000 CT scans collected from five international research centers. The dataset was labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.
Lama Nachman, is an Intel Fellow & Director of Anticipatory Computing Lab. Lama is best known for her work with Prof. Stephen Hawking, she was instrumental in building an assistive computer system to assist Prof. Stephen Hawking in communicating. Today she is assisting British roboticist Dr. Peter Scott-Morgan to communicate. In 2017, Dr. Peter Scott-Morgan received a diagnosis of motor neurone disease (MND), also known as ALS or Lou Gehrig's disease. MND attacks the brain and nerves and eventually paralyzes all muscles, even those that enable breathing and swallowing.
On September 18th The Lancet Digital Health released an article called " Artificial intelligence in COVID-19 drug repurposing" (written by Yadi Zhou, PhD, Prof Fei Wang, PhD, Prof Jian Tang, PhD, Prof Ruth Nussinov, PhD, Prof Feixiong Cheng, PhD) in the article it explained how artificial intelligence is being used for drug reproposing,which is where an already existing is drug used to fight novel diseases such as COVID-19. Artificial intelligence is being used to speed up this process, with the exponential growth in computing power, memory storage and a plethora of data its only right for the medical sector to use this to speed up a process that help fight against the worlds latest threat that is COVID-19. So how is artificial intelligence being used to speed up this process, well the article explains how artificial intelligence used for extracting hidden patterns and evidence from biomedical data, which otherwise would have been done manually saving a considerable amount of time. In connection to the medical sector, artificial intelligence in medicine may be racially biased. Just like explained above artificial intelligence has transformed the healthcare and has really cut the time on many aspects of medicine such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged in a matter of second which is just incredible, but like all good things it comes with its flaws.
IMAGE: Working in coal mines might make people more susceptible to serious complications from COVID-19. Larissa Casaburi, a researcher in the WVU School of Medicine, is using artificial intelligence to study... view more Artificial intelligence can do more than recommend a song or suggest what to write in an email. It might even be able to predict outcomes in COVID-19 patients. Larissa Casaburi, a researcher in the West Virginia University School of Medicine, is using artificial intelligence to study how being a coal miner affects COVID-19 outcomes. She's also investigating the ways smoking, vaping and having a chronic lung condition influence how COVID-19 patients fare.
Diagnosing emphysema and classifying its severity have long been more art than science. "Everybody has a different trigger threshold for what they would call normal and what they would call disease," said U. Joseph Schoepf, M.D., director of cardiovascular imaging for MUSC Health and assistant dean for clinical research in the Medical University of South Carolina College of Medicine. And until recently, scans of damaged lungs have been a moot point, he said. "In the past, if you lost lung tissue, that was it. The lung tissue was gone, and there was very little you could do in terms of therapy to help patients," he said.
Image classification is a challenging task for the visual content, particularly microscopic images for example histopathological images due to high convolution of inter-intraclass dependencies. The underlying structures are complex and interwoven due to similar structural morphological textures. Figure 1 presents some of the complex textures present in histopathology of images. Deep learning is prevalent due to its ability to learn features directly from the input, providing us a window to avoid arduous feature extraction processes Bengio et al.. One of the key features of deep learning is to discover abstract level features and then deep dive for extracting structural semantics in the feature map.
As in many other industries, health care is undergoing a digital transformation as providers, payers and others in the health care value chain leverage new technologies to improve patient care and access, increase efficiencies and reduce costs. The most promising of these technologies is artificial intelligence (AI), which offers profoundly greater power and sophistication than ever before and has the potential to truly revolutionize patient care. Artificial intelligence gives machines (e.g., computers, drones, robots) the ability to "think" and make decisions using real-time data. AI enables these machines to interpret the world around them, ingest and learn from information, make decisions based on what they've learned, and take appropriate action--all without human intervention. AI has become a part of our everyday lives whether we know it or not, helping us with everything from shopping and banking to booking trips to receiving deliveries. In health care, AI is already improving many aspects of the industry, from empowering patients to adhere to their care plan to helping detect diseases, from discovering new drugs to assisting in surgeries.
Brazil has been hard-hit by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Candido et al. combined genomic and epidemiological analyses to investigate the impact of nonpharmaceutical interventions (NPIs) in the country. By setting up a network of genomic laboratories using harmonized protocols, the researchers found a 29% positive rate for SARS-CoV-2 among collected samples. More than 100 international introductions of SARS-CoV-2 into Brazil were identified, including three clades introduced from Europe that were already well established before the implementation of NPIs and travel bans. The virus spread from urban centers to the rest of the country, along with a 25% increase in the average distance traveled by air passengers before travel bans, despite an overall drop in short-haul travel. Unfortunately, the evidence confirms that current interventions remain insufficient to keep virus transmission under control in Brazil. Science , this issue p.  Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February and 11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country. : /lookup/doi/10.1126/science.abd2161
A deep learning model--a form of artificial intelligence (AI)--was more accurate than the current clinical standard at predicting a person's 12-year risk of developing lung cancer. The model's predictions are based on chest radiograph images (CXRs) and basic demographic data (age, sex, and current smoking status) commonly available in electronic health records (EHRs). The findings are published in Annals of Internal Medicine. Lung cancer screening with chest computed tomography (CT) scans can prevent lung cancer death. However, Medicare's current standard to determine who is eligible for lung cancer screening CT misses most lung cancers. Furthermore, lung cancer screening participation is poor, with an estimated less than 5 percent of screening-eligible persons being screened.
Lung cancer is the number one cancer killer in the United States. It's often found too late and difficult to treat, but now new technology in North Texas, is giving patients the upper hand on the disease and in some cases, it can cure them in less than a day. Whether a dip in the pool or a workout in the gym, life as a retiree for 85-year-old Jere Bone is active and comfortably predictable. What he didn't predict was what doctors told him during a recent visit. "I was getting ready to leave and the doctor said, 'oh, by the way, has anyone mentioned that spot in your lung that looks like a might be a tumor?'" said Bone.