Deep learning proves effective in spotting liver masses in CT

The consternation of radiologists about the impact of artificial intelligence is real--but so are the benefits of machine learning. Recent research showed that deep learning with a convolutional neural network (CNN) was successful in differentiating liver masses in CT. The retrospective study, published online Oct. 23 in Radiology, examined the diagnostic abilities of a deep learning method with a CNN. Researchers tested the CNN with 100 liver mass image sets from 2016, including 74 men and 26 women with the average age of 66 years old. "This preliminary study, which used 55, 536 image sets (1068 image sets augmented by a factor of 52) to obtain models, indicated that classifying liver masses into five categories can be accomplished with a high degree of accuracy by using a deep learning method with a CNN on dynamic contrast-enhanced CT images," wrote Koichiro Yasaka, MD, PhD, with the department of radiology at the University of Tokyo Hospital in Japan, and colleagues.