Imaging Informatics

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 updated LR-5 criteria for Liver Imaging Reporting and Data System (LI-RADS) version 2018 can improve sensitivity for diagnosing small hepatocellular carcinomas (HCC) compared to LI-RADS 2017.

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.

In the first five months of the EHR, radiology information system (RIS) and PACS deployment project, Robert Paul, a radiology informatics manager at Mayo Clinic in Arizona, lost 60% of his team. He described his efforts to reduce burnout among his staff during a presentation at the SIIM annual conference.

Blockchain could be used to streamline preauthorization, share images between institutions and empower patients. But if healthcare as a whole isn't interested in sharing data, no technology can solve the industry's imaging informatics problems.

 

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

A new CT- and PET-imaging-based approach—one that entails applying big data to personalizing treatment protocols—is needed to better identify which head and neck carcinoma (HNC) patient subgroups respond to which specific therapies.

A recent study validating the 2017 version of the ultrasound Liver Imaging Reporting and Data System (US LI-RADS) for detecting hepatocellular carcinoma (HCC) identified a few limitations in its scoring.

“The fact that CAD significantly shortened interpretation time is important, especially if either state or federal legislation ends up mandating, or even recommending, additional screening with US for women with dense tissue on mammograms," wrote Priscilla J. Slanetz, MD, MPH, in an accompanying editorial.

The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) was originally created to improve patient management and avoid unnecessary fine needle aspiration biopsy in patients with thyroid nodules. However, its clinical use is still questioned.

Compressed sensing (CS) can reduce the typically long acquisition time of conventional ankle MRIs while preserving imaging quality, according to a small study published in the European Journal of Radiology.