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.
Utilizing an AI system for digital breast tomosynthesis (DBT) can improve radiologists’ accuracy while dramatically reducing reading times, according to a new study published in Radiology: Artificial Intelligence.
A deep learning platform created by researchers at the Dana-Farber Cancer Institute can identify cancer in radiology reports as well as clinicians, but in a fraction of the time, according to new research published July 25 in JAMA Oncology.
A deep learning classification approach can identify cancerous regions from benign areas in optical coherence tomography (OCT) images of breast tissue, according to results of a July 17 study published in Academic Radiology.
A deep learning model that simulates a clinician’s diagnostic process can accurately diagnose Alzheimer’s disease from cognitively normal patients, according to a study published July 16 in Neurocomputing.