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In a recent review published in the Journal of Human Genetics, a group of authors explored the potential of deep learning (DL), particularly convolutional neural networks (CNNs), in enhancing predictive modeling for omics data analysis, addressing challenges and future research directions.
Study: Advances in AI and machine learning for predictive medicine. Image Credit: NicoElNino/Shutterstock.com.
Scientists have developed a new robot that can ‘mimic’ the two-handed movements of care workers as they dress an individual.
Until now, assistive dressing robots, designed to help an elderly person or a person with a disability get dressed, have been created in the laboratory as a one-armed machine, but research has shown that this can be uncomfortable for the person in care or impractical.
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Techsomed’s thermal ablation therapy, a minimally invasive procedure for small tumors, has huge benefits over surgery or chemotherapy for patients and is a world first.
In a collaboration with Houston Methodist Hospital, researchers from the UH Engineering Robotic Swarm Control Laboratory led by Aaron Becker, assistant professor of electrical and computer engineering, are developing a novel treatment for pulmonary embolism (PE) using millimeter-scale corkscrew shaped robots controlled by a magnetic field. PE is the third most common cardiovascular disease, resulting in up to 300,000 deaths annually.
“Using non-invasive miniature magnetic agents could improve patient comfort, reduce the risk of infection and ultimately decrease the cost of medical treatments,” according to Julien Leclerc, a Cullen College research associate specializing in applied electromagnetics. “My goal is to quickly bring this technology into the clinical realm and allow patients to benefit from this treatment method as soon as possible.\.
New method will help scientists study neural networks and neuroplastic changes underlying higher brain functions and disease processes in model animals.
H/T Stephen Wolfram.
Particularly given its recent surprise successes, there’s a somewhat widespread belief that eventually AI will be able to “do everything”, or at least everything we currently do.
Stephen Wolfram explores the potential—and limitations—of AI in science. See cases in which AI will be a useful tool, and in others a less ideal tool.
For decades, scientists and pathologists have tried, without much success, to come up with a way to determine which individual lung cancer patients are at greatest risk of having their illness spread, or metastasize, to other parts of the body.
Now a team of scientists from Caltech and the Washington University School of Medicine in St. Louis has fed that problem to artificial intelligence (AI) algorithms, asking computers to predict which cancer cases are likely to metastasize. In a novel pilot study of non-small cell lung cancer (NSCLC) patients, AI outperformed expert pathologists in making such predictions.
These predictions about the progression of lung cancer have important implications in terms of an individual patient’s life. Physicians treating early-stage NSCLC patients face the extremely difficult decision of whether to intervene with expensive, toxic treatments, such as chemotherapy or radiation, after a patient undergoes lung surgery. In some ways, this is the more cautious path because more than half of stage I–III NSCLC patients eventually experience metastasis to the brain. But that means many others do not. For those patients, such difficult treatments are wholly unnecessary.
With a mobile app powered by artificial intelligence (AI), Caitlin Hicks, MD, MS, reviews selfies of patients’ feet in real time to track their wounds as part of a clinical trial. The app saves time for Hicks, a vascular surgeon at Johns Hopkins Medicine, but also reduces clinic trips for her patients with diabetes in inner-city Baltimore, many of whom are elderly and less mobile or have other socioeconomic barriers to care. Hicks knows that for these patients, wound vigilance is the linchpin to preventing infection, hospitalization, or, worse, amputation or even death.
Despite their crushing toll, diabetic foot infections remain stubbornly hard to treat, but multidisciplinary care teams, new drugs and devices on the horizon, and practical solutions to socioeconomic factors could budge the needle.
Summary: A recent study showcases a significant leap in the study of brain oscillations, particularly ripples, which are crucial for memory organization and are affected in disorders like epilepsy and Alzheimer’s. Researchers have developed a toolbox of AI models trained on rodent EEG data to automate and enhance the detection of these oscillations, proving their efficacy on data from non-human primates.
This breakthrough, stemming from a collaborative hackathon, offers over a hundred optimized machine learning models, including support vector machines and convolutional neural networks, freely available to the scientific community. This development opens new avenues in neurotechnology applications, especially in diagnosing and understanding neurological disorders.
