English
Arabic
Chinese (Simplified)
Dutch
Esperanto
French
German
Greek
Italian
Japanese
Korean
Portuguese
Russian
Spanish
UrduJohns Hopkins Medicine researchers have published new research that reports on a potential alternative and less-invasive approach to measure intracranial pressure (ICP) in patients.
Tag: Deep Learning Algorithm
Optical Imager Captures Amplitude and Phase Information without Digital Processing
UCLA researchers introduced an all-optical complex field imager that captures both amplitude and phase information of optical fields using an intensity-based sensor array. This device employs optimized diffractive surfaces to eliminate the need for digital processing in conventional complex imaging techniques, improving imaging speed and reducing computational demand.
New Studies: AI Captures Electrocardiogram Patterns That Could Signal a Future Sudden Cardiac Arrest
Two new studies by Cedars-Sinai investigators support using artificial intelligence (AI) to predict sudden cardiac arrest—a health emergency that in 90% of cases leads to death within minutes.
AI identifies antimalarial drug as possible osteoporosis treatment
Artificial intelligence is being harnessed by some scientists to predict which molecules could treat illnesses. Researchers reporting in ACS Central Science have used one such deep learning algorithm, and found that an antimalarial drug could treat osteoporosis.
New cyber algorithm shuts down malicious robotic attack
Australian researchers have designed an algorithm that can intercept a man-in-the-middle (MitM) cyberattack on an unmanned military robot and shut it down in seconds.
Mount Sinai Researchers Use New Deep Learning Approach to Enable Analysis of Electrocardiograms as Language
Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that allows for the interpretation of ECGs as language. This approach can enhance the accuracy and effectiveness of ECG-related diagnoses, especially for cardiac conditions where limited data is available on which to train. In a study published in the June 6 online issue of npj Digital Medicine DOI: 10.1038/s41746-023-00840-9, the team reported that its new deep learning model, known as HeartBEiT, forms a foundation upon which specialized diagnostic models can be created. The team noted that in comparison tests, models created using HeartBEiT surpassed established methods for ECG analysis.
Using machine learning to help monitor climate-induced hazards
Combining satellite technology with machine learning may allow scientists to better track and prepare for climate-induced natural hazards, according to research presented last month at the annual meeting of the American Geophysical Union.
Automated epilepsy lesion detection on MRI: The MELD Project
In this episode of Sharp Waves, the ILAE podcast, Dr. Maryam Nabavi Nouri talks with Dr. Konrad Wagstyl about the MELD Project, an open-science consortium using deep learning principles to develop automated lesion detection of clinical MRI data.
Mount Sinai Researchers Use Artificial Intelligence to Uncover the Cellular Origins of Alzheimer’s Disease and Other Cognitive Disorders
Deep learning models represent “an entirely new paradigm for studying dementia”
Scientists Create a Labor-Saving Automated Method for Studying Electronic Health Records
Scientists at the Icahn School of Medicine at Mount Sinai described the creation of a new, automated, artificial intelligence-based algorithm that can learn to read patient data from electronic health records. In a side-by-side comparison, they showed that their method, called Phe2vec (FEE-to-vek), accurately identified patients with certain diseases as well as the traditional, “gold-standard” method, which requires much more manual labor to develop and perform
NSF makes $20 Million investment in Optimization-focused AI Research Institute led by UC San Diego
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.
Deep Learning Trains Buildings to Optimize Efficiency
New approach allows nonexperts to optimize control of a building’s energy systems without adding computing power or proprietary software.