In total, 7,774 images taken from 287 patients were used to train the deep learning model, according to a study published in AJR.

The neural network requires only a fraction of the data typically needed for normal MR imaging exams.

It can also help trainees improve their scores on the Fundamentals of Laparoscopic test, which must be passed before receiving general surgery certification.

The hybrid platform proved more accurate at detecting enlarged heart cases than machine learning or a human reader working independently.

The algorithms will analyze various pieces of information, including CT images and vital signs, to help clinicians determine disease severity and predict patient outcomes.

Given that the disease is one of the most common cancers among women, these results may help personalize care plans and avoid invasive options for many struggling with treatment decisions, experts wrote in JAMA Network Open.

Massachusetts General Hospital doctors say their automated pulmonary x-ray severity-scoring algorithm can help radiologists optimize pandemic workflows and manage critical resources, such as ventilators.

The deep learning hybrid may particularly help less-experienced readers or trainees spot subtle femoral neck breaks, experts wrote in the Journal of Digital Imaging.

H. Lee Moffitt Cancer Center and Research Institute scientists used low-dose CT and chest x-ray imaging data from the National Lung Screening Trial to create their model.

The agreement affords Life Image’s network of 58,000 global clinics—including primary and comprehensive stroke centers—access to RapidAI’s data-driven neuroimaging technology.

Typically, distinguishing between tumor types involves immunohistochemical sample staining and complex genetic analysis, often a day-long process.

An automated system to predict knee replacement surgery risk is critical, given that more than half of the estimated 14 million Americans suffering from knee osteoarthritis will undergo the procedure.