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.
Engineers from Duke University have harnessed the power of machine learning to increase the resolution of optical coherence tomography (OCT) imaging, according to an Aug. 19 study published in Nature Photonics.
A machine learning method trained on synthetic breast ultrasound elastography images accurately classified tumors when applied to real-world images, according to a new study published in the August issue of Computer Methods in Applied Mechanics and Engineering.
A new machine learning system created by UCLA researchers may help doctors classify breast cancers that are notoriously difficult to diagnose, according to an Aug. 9 study published in JAMA Network Open.
Machine learning algorithms can classify free-text pathology reports at the organ level and are easily interpreted by human readers, according to an Aug. 7 study published in Radiology: Artificial Intelligence.
A neural network model can scour electronic medical record (EMR) data and determine if a patient has imaging-specific pulmonary embolism (PE)—a potential remedy for unnecessary CT imaging, reported authors of a multicenter study published in JAMA Network Open.