Researchers working for the U.S. government have used deep learning to evaluate stem cell-derived tissue samples, sharing their findings in the Journal of Clinical Investigation.

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.

XACT Robotics, a radiology technology company with offices in the United States and Israel, raised $36 million in its latest financing round.

That’s according to a new survey of healthcare stakeholders, highlighted in November’s European Journal of Radiology. 

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.