Ensembles created using models submitted to the RSNA Pediatric Bone Age Machine Learning Challenge convincingly outperformed single-model prediction of bone age.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
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.
An algorithm did better than experts radiologists at finding tiny brain hemorrhages in head scans — an advance that one day may help doctors treat patients with strokes.
An AI tool identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists.
Based on a convolutional neural network the tool is able to provide results within seconds, thus supporting the doctor with comprehensive image analysis.
Scientists have now produced tiny diamonds, so-called "nanodiamonds", which could serve as a platform for both the therapy and diagnosis of brain diseases.
Biomedical engineers have developed a portable optical coherence tomography scanner that promises to bring the vision-saving technology to underserved regions.
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.
Researchers have developed a technology to help clinicians "see" and map patient pain in real-time, through special augmented reality glasses.
Researchers are pairing a nanoscale imaging technique with virtual reality technology to create a method that allows researchers to “step inside” their biological data.
Machine learning has the potential to vastly advance medical imaging, particularly CT scanning, by reducing radiation exposure and improving image quality.
Researchers have created new AI software that can identify cardiac rhythm devices in x-rays more accurately and quickly than current methods.
Researchers announce critical advances in the use of 3D-printed coronary phantoms with diagnostic software, further developing a non-invasive diagnostic method for Coronary Artery Disease risk assessment.
Patients could soon get faster and more accurate diagnoses with new software that can automatically detect signs of diabetes, heart disease and cancer from medical images.
At ECR 2019, researchers talked about the practical applications of mixed realities in medical education and training as well as preprocedural planning and visualization during a surgery.
Researchers have developed a system using artificial intelligence to quickly diagnose and classify brain hemorrhages and to provide the basis of its decisions from relatively small image datasets.
SubtlePET’s AI-powered technology allows hospitals and imaging centers to enhance images from faster scans leading to an improved patient experience during imaging procedures.
The Murab project is developing technology that will make it possible to take more accurate biopsies and diagnose cancer and other illnesses faster.