Artificial Intelligence

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.

In order to properly train and validate algorithms, developers need high volumes of quality-labeled data. But such datasets are not easy to obtain.

Predictions of heart attacks and deaths based on coronary computed tomography angiography (CCTA) are more accurate when made using an artificial intelligence (AI) algorithm than with the Coronary Artery Disease Reporting and Data System (CAD-RADS) or other risk assessment methods.

Canon Medical Systems USA has received FDA approval for its deep convolutional neural network (DCNN) image reconstruction technology for CT scanning.

“In a scenario where double reading at screening mammography is not available…we believe that the use of this model as a second reader could be beneficial,” wrote researchers in a new study published by Radiology.