Artificial Intelligence
New Algorithm Predicts IQ Scores Using fMRI Brain Scans
A team of researchers from the California Institute of Technology, Cedars-Sinai Medical Center, and the University of Salerno have developed a machine-learning algorithm that can predict a person's intelligence by analyzing functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. More...28 Aug 2018
Intel and Philips Partner to Speed Up Imaging Analysis Using AI
Intel Corporation and Royal Philips have tested two healthcare use cases for deep learning inference models: one on X-rays of bones for bone-age-prediction modeling and the other on CT scans of lungs for lung segmentation. In these tests, which were conducted using Intel Xeon Scalable processors and the OpenVINO toolkit, the researchers achieved a speed improvement of 188 times for the bone-age-prediction model and 38 times for the lung-segmentation model over the baseline measurements. These tests show that healthcare organizations can implement artificial intelligence (AI) workloads without expensive hardware investments. More...22 Aug 2018
Facebook Collaborates with NYU School of Medicine to Make MRI Scans Faster
Facebook, Inc. and the NYU School of Medicine’s Department of Radiology have announced a new collaborative research project named fastMRI that will investigate the use of artificial intelligence (AI) for making magnetic resonance imaging (MRI) scans up to 10 times faster. The project aims at making MRI technology available to more people, thereby expanding access to the key diagnostic tool. More...22 Aug 2018
Guerbet Partners with Imalogix on Dose Optimization with AI
Guerbet LLC USA, the US affiliate of the the Guerbet Group, which specializes in contrast product and solutions for medical imaging, has entered into a commercial partnership with Imalogix, a provider of artificial intelligence (AI) process and workflow solutions. More...20 Aug 2018
Deep Learning-Based System Detects and Classifies Mammogram Masses
Researchers from the Kyung Hee University have developed a fully integrated computer-aided diagnosis (CAD) system that uses deep learning and a deep convolutional neural network (CNN) to detect, segment and classify masses from mammograms. In a new study published by the International Journal of Medical Informatics, the researchers have described the use of their regional deep learning model, You-Only-Look-Once (YOLO), to detect breast mass from entire mammograms. The researchers then went on to use a new deep network model based on a full resolution convolutional network (FrCN), to segment the mass lesions pixel-to-pixel. Finally, a deep CNN was used to recognize the mass and classify it as either benign or malignant. More...16 Aug 2018
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The Artificial Intelligence channel of HospiMedica keeps the reader informed about the latest news in AI-based clinical decision making, Medical knowledge engineering, Intelligent medical information systems and additional related fields.