Two recent studies predict that computer-aided diagnosis schemes will likely be incorporated into PACS in the future.
Two recent studies predict that computer-aided diagnosis schemes will likely be incorporated into PACS in the future.
In one paper, Kunio Doi, Ph.D., director of Kurt Rossmann Laboratories for Radiologic Image Research at the University of Chicago, predicts that CAD schemes could be assembled as packages and implemented as a part of PACS. A package for chest CAD, for example, may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs, as well as computerized classification of benign and malignant nodules and differential diagnosis of interstitial lung diseases (Comput Med Imaging Graph 2007;31(4-5):198-211). Doi notes the potential to search PACS for differential diagnoses once researchers develop a reliable method to quantify the similarity of a pair of images.
The second paper reports a CAD/PACS integration toolkit designed to integrate CAD results with clinical PACS (Comput Med Imaging Graph 2007;31(4-5):195-197). One version uses the DICOM secondary capture object model to convert the screenshot of CAD results to a DICOM image file for PACS workstations to display. A second version converts CAD results to a DICOM structured report based on IHE workflow profiles, according to lead author Dr. Zheng Zhou of the Image Processing and Informatics Laboratory at the University of Southern California.
Breast MRI and Background Parenchymal Enhancement: What a Meta-Analysis Reveals
May 29th 2025Moderate or marked background parenchymal enhancement (BPE) reduces the sensitivity and specificity of MRI for breast cancer detection by more than 10 percent in comparison to scans with minimal or mild BPE, according to a new meta-analysis.
Lunit Unveils Enhanced AI-Powered CXR Software Update
May 28th 2025The Lunit Insight CXR4 update reportedly offers new features such as current-prior comparison of chest X-rays (CXRs), acute bone fracture detection and a 99.5 percent negative predictive value (NPV) for identifying normal CXRs.