The concept derives image-based biomarkers from MRI scans to produce a heat map for surgeons to better pinpoint cancers.

Underfunding and a lack of resources have led to significant problems imaging severely injured patients in trauma centers across the UK's health system. The Royal College of Radiologists has called on the government to address the issues.

Radiomic analysis can extract mounds of information from MRIs and help researchers determine if a patient’s cancer is likely to return 10 years after treatment.  

Machine learning is more accurate at predicting the long-term risk of potentially life-threatening cardiac events compared to standard clinical assessments, and eventually may revolutionize cardiovascular care.

The new approach offers more comprehensive information with no loss of image quality, and may alter the care landscape for patients with coronary artery disease.

More individuals are receiving cardiac implanted devices than ever before, and as a result, the number of complications stemming from the wire connecting the device to the heart—known as a lead—are also increasing.

The deep learning-based model yielded a lower false-negative rate for more aggressive cancers compared to traditional approaches.

The ACR analyzed a small sample of mammograms completed at Raleigh Radiology Blue Ridge over a two year period, finding quality problems in some images.

The Tokyo-based company hopes to add scale to better compete with the top global players in imaging. 

Combining fluorodeoxyglucose PET with high-resolution CT can help predict subtypes of early lung adenocarcinoma—a form of cancer that is on the rise.