The ambitious project seeks to exponentially increase new data within the U.K. Biobank, a prominent large-scale biomedical database and research resource. 

A machine learning system has come along that needs no human labeling of data for training yet matches radiologists at classifying diseases on chest X-rays—including some that the model was not specifically taught to detect.

The acquisition represents a significant advance in interoperability and will combine networks that total over 80 billion images globally, including data from institutions such as Mayo Clinic, Cleveland Clinic and New York Presbyterian Hospital.

With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

Suggesting the move will significantly advance radiology’s specialtywide imperative to “ditch the disk,” Montreal-based Intelerad Medical Systems has announced it is acquiring a longtime competitor in the image-exchange space.

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging. 

In this cohort, experts found that just 34% of surgeries were performed on symptomatic patients, which shifted their focus to other potential modes of detection.

To date, research pertaining to reactive lymphadenopathy has focused mostly on patients with cancer who are routinely staged and monitored via PET/CT or in women undergoing breast cancer screening and/or imaging with mammography and ultrasound.