
Biomedical research: deep learning outperforms machine learning
Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.
Scientists have presented a new method for configuring self-learning algorithms for a large number of different imaging datasets – without the need for specialist knowledge or very significant computing power.
Researchers have developed an AI tool that can measure the volume of cerebral ventricles on MRIs in children within about 25 minutes.
AI offers not only the possibility of better detection of a tumor, a skin lesion or some other indication but also can improve accuracy and efficiency for radiologists.
An artificial intelligence-based detects early stages of Alzheimer’s through functional magnetic resonance imaging.
Researchers have developed a unique diagnostic tool that can detect dystonia from MRI scans, the first technology of its kind to provide an objective diagnosis of the disorder.
Researchers have applied these artificial intelligence techniques to autism diagnosis.
Researchers have created a machine learning algorithm that can detect subtle signs of osteoarthritis on an MRI scan taken years before symptoms even begin.
Researchers have developed an AI technique that will protect medical devices from malicious operating instructions in a cyberattack.
Researchers used AI and genetic analyses to examine the structure of the inner surface of the heart using 25 000 MRI scans.
Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
A new AI approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report.
Researchers have presented a method that could greatly accelerate dynamic magnetic resonance imaging of blood flow.
Scientists have developed an innovative new technique that uses artificial intelligence to better define the different sections of the brain in newborns during a magnetic resonance imaging (MRI) exam.
Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,
Deep learning can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD).
Researchers are using laser scalpels and precision robotics to make tattoo removal faster, more accurate and less painful.
Researchers have developed a new method to guide endovascular instruments into complex vascular structures that were inaccessible to endovascular surgeons until now.
Scientists correlate neuronal activity in the human entorhinal cortex with place-based memories; finding sheds new light on how the brain processes spatial memory.
Though identifying data typically are removed from medical image files before they are shared for research, a study finds that this may not be enough to protect patient privacy.
Scientists have identified mechanisms in the human brain that could help explain the the unsettling feeling we get from robots and virtual agents that are too human-like.
A researcher developed a 3D printed baby dummy, based on an MRI scan of a real newborn baby, which could improve the training of the reanimation procedure.
Medical software that overlays tumour information from MRI scans onto ultrasound images can help guide surgeons conducting biopsies and improve prostate cancer detection.
The Murab project is developing technology that will make it possible to take more accurate biopsies and diagnose cancer and other illnesses faster.
Researchers are using artificial intelligence to reduce the dose of a contrast agent that may be left behind in the body after MRI exams, according to a study presented at RSNA.
Sossena Wood 3D, Pitt bioengineering grad student, prints a phantom head for testing 7T MRI imaging in the Radiofrequency Research Facility.
Researchers at Johns Hopkins University have successfully performed 3D personalized virtual simulations of the heart.
A team at the University of Auckland's Bioengineering Institute has created a virtual 3D heart that could have a major impact on treatment of the most common heart rhythm disturbance, atrial fibrillation (AF).
An engineer designed the first neurosurgical robotic system capable of performing bilateral stereotactic neurosurgery inside a MRI scanner.