A deep learning platform can accurately distinguish critical from non-critical feeding tube placement on radiographs, according to a recent study published in the Journal of Digital Imaging.

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.

Medtronic and Viz.ai, a growing leader in artificial intelligence, are collaborating on a new software to automatically alert specialists when a stroke is identified during a CT scan.

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.

“The CXR-risk score took as input the radiograph only. This was intended to prove a point—that a CNN can extract prognostic information embedded in the image, without any other demographic or clinical information,” wrote authors of a new study published in JAMA Network Open.

A newly created three-dimensional (3D) neural network can improve the detection of pulmonary nodules on CT scans, according to a study published July 12 in PLOS ONE. 

The algorithm improved the specificity of thyroid biopsy recommendations, beating seven of nine radiologists. With more research, the algorithm could help in the decision-making process for assessing thyroid nodules.

While AI wasn’t the only topic discussed during the SIIM 2019 annual meeting, every issue seemed to be tied to the emerging technology in one way or another.

Data security has become a serious issue in the U.S., not only for big tech companies like Facebook, but for vendors and institutions looking to use patient imaging information to develop AI platforms.

The American College of Radiology (ACR) has expanded its ACR AI-LAB pilot program geared toward helping radiologists develop AI models without the use of coding language.

A convolutional neural network (CNN) approach can accurately identify and sub-classify suspected tuberculosis (TB) on chest radiographs, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting.