Just the beginning: 6 applications for machine learning in radiology beyond image interpretation Posted on : Nov 18 - 2017

Discussions about machine learning’s impact on radiology might begin with image interpretation, but that’s only the tip of the iceberg. When it comes to realizing the technology’s full potential, it’s like Bachman Turner Overdrive sang many years ago: You ain’t seen nothing yet.

The authors of a new analysis published in the Journal of the American College of Radiology wrote at length about the many applications of machine learning.

“Machine learning has the potential to solve many challenges that currently exist in radiology beyond image interpretation,” wrote lead author Paras Lakhani, MD, department of radiology at Thomas Jefferson University Hospital in Philadelphia, and colleagues. “One of the reasons there is great excitement in radiology today is the access to digital Big Data. Many institutions have implemented electronic health care databases over the past two decades, including for images in PACS, radiology reports and ordering information in Radiology Information Systems, and electronic health records that encompass information from other sources, including clinical notes, laboratory data and pathology records. Moreover, radiology images themselves are rich in metadata stored in the DICOM format, which may be leveraged as well. As such, there are great opportunities to uncover complex associations within the data using machine learning that would otherwise be difficult for a human to do.”

These are some of the many examples Lakhani et al. provided of how machine learning can be used in radiology beyond image interpretation:

1. Creating safety protocols

Developing safety protocols is an important part of any radiologist’s job, the authors noted, and machine learning can help speed up the entire process.

“This can be a time-consuming but important task,” the authors wrote. “However, recent studies demonstrate that machine learning algorithms utilizing information extracted from the provided study indications can be accurate in determining protocols of studies in both brain and body MRIs.”

2. Decreasing radiation dose in CT

Decreasing radiation dose is a huge topic in medical imaging. Lakhani et al. noted that deep learning has the potential to help specialists lower dose without the usual tradeoff of producing “poorer-quality images.”

“The idea is to train a classifier to map ‘noisy’ images generated from ultralow-dose CT protocols to high-quality images from regular protocols, using deep learning techniques,” the authors wrote. “This is akin to creating ‘super-resolution’ photorealistic images from down-sampled versions, which has already shown exciting results in every-day color images.” View More