Technological turmoil

Deep Learning and AI will redefine radiology

While there has been a lot of hype — and even fear — about the role deep learning (DL) and artificial intelligence (AI) play in radiology, the reality is that they are both potentially useful technologies that will add value to the specialty in a number of ways.

"Deep Learning is not going to replace us," said Paul Chang, MD, of the University of the Chicago School of Medicine, during a Sunday session on DL and AI in radiology. "But it will redefine us." And radiology will need this technology more than ever due to the increasing demands on clinical imaging. Data sets are getting more complex and there is an increasing need to correlate images with other clinical information in order to implement practices such as radiogenomics, Dr. Chang said.

"So deep learning will help us because we are going to need something — we need some tool — some mechanism — to meet these new imaging challenges," Dr. Chang said. "We are going to need some kind of cybernetic help to get through a day's work and help us maintain and improve quality."

Infrastructure remains a challenge

Deep Learning can mean 'very capable' or deep as in 'deep waters' or 'obscure,' and that's the problem

Paul Chang

But these are early days when it comes to incorporating DL and AI into the practice of radiology, and numerous challenges still exist. For example, how can radiology confidently validate the performance of these new technologies? "Deep learning is a great name for it because it has two meanings," Dr. Chang said. "It can mean 'very capable' or deep as in 'deep waters' or 'obscure,' and that's the problem. There are very deep layers to deep learning systems and it's very difficult to understand why they work."

Comprehending DL requires the use of cases and tons of data. But radiologists really can't get compelling use cases unless they have the necessary data and infrastructure, Dr. Chang said. Which brings up another challenge. Radiology doesn't have the infrastructure to either feed, train or consume these systems. "Other industries have really revved up for cloud computing and big data and are ready to consume deep learning, because deep learning loves that kind of environment," Dr. Chang said. "Radiology is still struggling with electronic medical records (EMRs) and PACS and we generally don't have a true IT infrastructure that can feed and consume these systems."

The specialty should first pursue a "hedge strategy" by building infrastructures necessary to prepare for the cloud and big data, registries and advanced analytics, as well as DL, he said. "The bottom line is that deep learning won't replace people — it will enhance them," Dr. Chang said. "We should be looking for the minimally heuristic use case sweet spot like workflow optimization. Something that isn't sexy, but is an easy win, saves money, and improves lives." For those still unsure how DL fits into the healthcare landscape, Dr. Chang offers another comparison: "The analogy I use is the gold rush," he said. "Everyone went out west to dig for gold. Most miners either failed or died, but there were people who thrived — the people selling the miners the shovels. You needed to build an infrastructure."


Copyright: RSNA Daily Bulletin/Mike Bassett

28.11.2017

Related articles

Improvement

Big data and its role for radiology

Big data has the potential to offer a better understanding of how to aggregate clinically relevant data on a large scale and deliver better computer aided diagnosis algorithms and tools.

Machine learning

Google AI now can predict cardiovascular problems from retinal scans

Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary.…

Smart techniques

Machine learning is starting to reach levels of human performance

Machine learning is playing an increasing role in computer-aided diagnosis, and Big Data is beginning to penetrate oncological imaging. However, some time may pass before it truly impacts on clinical…

Related products

R/F digital - Digital

Agfa - DR 600 (ceiling suspended)

Agfa HealthCare

R/F digital - Digital

Agfa - DX-D 300

Agfa HealthCare

R/F digital - Digital

Agfa - DX-D 40 detector

Agfa HealthCare

RIS / PACS

Agfa Enterprise Imaging

Agfa HealthCare

Accessories / Complementary systems

Agfa Enterprise Imaging Business Intelligence

Agfa HealthCare

Accessories / Complementary systems

Agfa Enterprise Imaging Exchange

Agfa HealthCare