A deep convolutional neural network (DCNN) can be trained to analyze MRI scans and differentiate between tuberculous spondylitis and pyogenic spondylitis, according to a new study published in Scientific Reports.

Toronto-based Exact Imaging and Cambridge, U.K.-based Cambridge Consultants have announced a new international partnership focused on improving the way specialists visualize and detect prostate cancer.

The creators of a fully automated medical image analysis program to detect breast tumors was inspired by the classic video game Tetris. It is also almost twice as fast at finding lesions as existing techniques and just as accurate.

A deep learning system (DLS) designed to assess liver fibrosis using CT images was found to be highly accurate, according to a new study published in Radiology.

Fujifilm Corporation and the Indiana University School of Medicine in Indianapolis have announced a new research agreement that will focus on applying artificial intelligence (AI) to medical imaging diagnostic support systems.

Researchers at the University of Central Florida’s Center for Research in Computer Vision have created an artificial intelligence (AI) algorithm that can detect specks of lung cancer in CT scans with 95 percent accuracy.

Intel and Philips announced that they have joined forces to work on artificial intelligence (AI) by using Intel’s Xeon Scalable processors and OpenVINO toolkit to test two use cases for deep learning inference models.

Computer-aided diagnosis (CAD) powered by artificial intelligence can accurately assess diminutive colorectal polyps, according to a new study published in Annals of Internal Medicine. But is the CAD’s performance level high enough that specialists can follow the recommended “diagnose-and leave” strategy for diminutive polyps?