The boost in efficiency for measuring such scarring could make it easier for clinicians to overcome the time-consuming process of quantifying late gadolinium enhancement (LGE)—a proven predictor of hypertrophic cardiomyopathy.

In fact, clinicians who took a second look at x-rays using the deep learning software improved their sensitivity, on average, by 5.2%.

The novel method uses a deep neural network to improve fluorescence lifetime imaging, which allowed scientists at Rensselaer Polytechnic Institute to view molecular-level interactions within cells.

Besides pinpointing microfractures, the researchers believe combining color spectral CT imaging with their novel nanoparticles could help detect more serious problems such as heart blockages.

"The 7T MRI scanner affords us new ways of viewing areas of damage in neurologic diseases such as MS that were not well seen using 3T MRI," corresponding author Jonathan Zurawski, MD, said.

Radiologists, medical students and surgeons all agree that AI should be incorporated into diagnostic radiology, but for the most part their perceptions of the technology are drastically different.

When we go to sleep at night, our brains are wiped clean of harmful toxins. Boston-area researchers now have the evidence to prove it, thanks in part to high-resolution imaging.

Deep learning can identify cancerous and precancerous esophagus tissue on digitized pathology slides, opening the door for AI to alter the digital pathology landscape.