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This Squishy 3D-Printed Human Heart Feels Like the Real Thing

WIRED

In the intro to the HBO sci-fi series Westworld, a 3D printer churns out humanoid robots, delicately assembling the incredible complexities of the human form so that those robots can go on to--spoiler alert--do naughty things. It takes a lot of biomechanical coordination, after all, to murder a whole lot of flesh-and-blood people. Speaking of: Researchers just made a scientific leap toward making 3D-printed flesh and blood a reality. Writing recently in the journal ACS Biomaterials Science & Engineering, a team described how they repurposed a low-cost 3D printer into one capable of turning an MRI scan of a human heart into a deformable full-size analog you can actually hold in your hand. Squeeze it, and it'll give like the real thing.


The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings

#artificialintelligence

To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality. Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016–June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Software created for image enhancement, reporting, or workflow management was excluded. Software was categorized by task (repetitive, quantitative, explorative, and diagnostic), modality, and subspecialty.


AI that can diagnose tinnitus from brain scans may improve treatment

#artificialintelligence

An artificial intelligence that can diagnose tinnitus based on the results of brain imaging, rather than subjective tests, may help improve treatments for the condition. Mehrnaz Shoushtarian at the Bionics Institute in Melbourne, Australia, and her colleagues have developed an algorithm that can detect whether a person has tinnitus, and also how severe it is. The AI can spot the presence of tinnitus with 78 per cent accuracy, and distinguish between mild and severe forms with 87 per cent accuracy. Chronic tinnitus affects around 15 per cent of adults. The condition is usually diagnosed by a hearing test, by self-reporting or based on a subjective questionnaire.


With deep learning algorithms, standard CT technology produces spectral images

#artificialintelligence

Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians' "vision." In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.


DarwinAI,Red Hat Team Up to Bring COVID-Net Radiography Screening AI

#artificialintelligence

DarwinAI, the explainable artificial intelligence (XAI) company, and Red Hat, the world's leading provider of open source solutions, announced a collaboration to accelerate the deployment of COVID-Net--a suite of deep neural networks for COVID-19 detection and risk stratification via chest radiography--to hospitals and other healthcare facilities. DarwinAI and Red Hat are also leveraging the expertise of a computation research group, the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children's Hospital to better focus the software for real world clinical and research use. "The COVID-Net system is a promising tool, but needs to be coupled with a compelling GUI to be effective -- Boston Children's ChRIS framework and the Red Hat OpenShift platform provides an effective way to get COVID-Net into the hands of health care professionals on the front lines." Since the launch of COVID-Net by DarwinAI and the University of Waterloo's Vision and Imaging Processing (VIP) Lab, the project has continued to evolve with assistance, participation and collaboration from researchers and clinicians around the world. The initiative eventually led to a collaboration between DarwinAI and Red Hat, using underlying technology from Boston Children's, the number one pediatric hospital in the nation.


The Nuances Of Developing AI For Medical Imaging

#artificialintelligence

Machine learning has shown significant improvement in healthcare. Researchers have developed models that can diagnose critical conditions like diabetic eye disease or metastatic breast cancer. The computer vision has been even tried for AR assisted surgeries. But why don't we see more AI in healthcare? Challenges are plaguing the ML community.


How humans store memories is 'cornerstone of our intelligence'

Daily Mail - Science & tech

Scientists believe they may have discovered the'cornerstone of human intelligence', and it is all down to how we create and store memories. Previous research shows animals use a technique called'pattern separation' which stores memories in separate groups of neurons in the hippocampus. This stops them from getting mixed up, and it was believed humans probably use this technique as well. But a new study by experts at the University of Leicester shows the same group of neurons in the hippocampus store all memories. This key difference, the researchers say, could be the single factor which allowed our intellect to surpass that of other animals.


Data science pathway prepares radiology residents for machine learning

#artificialintelligence

A recently developed data science pathway for fourth-year radiology residents will help prepare the next generation of radiologists to lead the way into the era of artificial intelligence and machine learning (AI-ML), according to a special report published in Radiology: Artificial Intelligence. AI-ML has the potential to transform medicine by delivering better and more efficient healthcare. Applications in radiology are already arriving at a staggering rate. Yet organized AI-ML curricula are limited to a few institutions and formal training opportunities are lacking. Three senior radiology residents at Brigham and Women's Hospital (BWH) in Boston recently helped devise a data science pathway to provide a well-rounded introductory experience in AI-ML for fourth-year residents.


With deep learning algorithms, standard CT technology produces spectral images – IAM Network

#artificialintelligence

Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians' "vision." In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.


How Might Artificial Intelligence Applications Impact Risk Management?

#artificialintelligence

Artificial intelligence (AI) applications have attracted considerable ethical attention for good reasons. Although AI models might advance human welfare in unprecedented ways, progress will not occur without substantial risks. This article considers 3 such risks: system malfunctions, privacy protections, and consent to data repurposing. To meet these challenges, traditional risk managers will likely need to collaborate intensively with computer scientists, bioinformaticists, information technologists, and data privacy and security experts. This essay will speculate on the degree to which these AI risks might be embraced or dismissed by risk management.