Convolutional neural networks (CNNs) can accurately identify vertebral fractures (VFs) on x-rays, according to a Sept. 17 study published in Radiology. The method may improve radiologists’ diagnostic ability.
A new machine learning approach can predict the negative side effects of radiation treatment in patients with head and neck cancers. The findings, presented at the American Society for Radiation Oncology (ASTRO) annual meeting, can help select patients who might need a more tailored care approach.
A team of researchers from Taiwan performed a first-of-its-kind external validation of four AI algorithms used to detect pulmonary nodules in chest x-rays, sharing their results in Clinical Radiology. The classifiers could help radiologists improve medical imaging care as a whole.
A new study found that machine learning networks can learn to alter images so they are indistinguishable from real ones. Researchers warned this may tempt criminals to use such platforms for cybersecurity attacks.
Physicians with no coding experience are able to create AI algorithms to classify medical images at levels comparable to state-of-the-art platforms, according to a new study published in The Lancet Digital Health. However, some experts questioned whether those without experience should really be creating such technology.
“Assessing (prostate cancer) PCa invasiveness as early as possible is essential for disease management, treatment choice, and patient prognosis,” wrote the authors of a new study published in Clinical Radiology.
AI is central to many large technology companies such as Facebook and Google, and may soon have a similar role in the medical imaging world, argued a group of radiologists in a new editorial published in Clinical Imaging.
A model utilizing natural language processing and machine learning can accurately detect radiology reports that demand follow-up imaging, reported researchers of a new study published in the Journal of Digital Imaging.
Researchers from the University of Oxford have created a new biomarker based off of coronary CT angiography (CCTA) images that can select patients at a high risk of heart attack five years before they occur.
A new radiomics-based machine learning model can evaluate immunohistochemistry (IHC) features and CT images to predict the presence of thyroid nodules, according to a new study published in the American Journal of Roentgenology.