The authors of the study hypothesized that they would find malalignments due to the nature of how the 2017 Fleischner Society guidelines, in that they do not pertain to incidentally detected nodules.

“Our approach has the potential to facilitate timely diagnoses and management of pancreatic lesions encountered in routine clinical practice,” experts involved in the study said.

A fully automated body composition analysis derived from CT imaging can be a valuable pretreatment tool for patients with colorectal cancer. 

In a close collaboration with radiation oncologists, experts trained their model on the CT lung images of 787 patients and tested it on the scans of more than 1,300 patients from external datasets.

The new analysis included 43,000 eligible U.S. veterans, 32% of whom were observed to have declined annual CT screening for lung cancer.

Prior attempts at imaging large fossils such as mammoth tusks failed to capture the full artifact with just one scan, instead requiring multiple partial scans that were subsequently pieced together.

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains how radiology vendors have developed AI app stores to make it easier to access new FDA cleared AI algorithms.
 

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains 5 developments to watch for in radiology artificial intelligence (AI).