A Scanner Smartly: How Artificial Intelligence Is Making Healthcare Imaging Quicker, Kinder And More Efficient

Dec. 2, 2019 - By: Sam Worley

The music festival Lollapalooza is held every year in downtown Chicago, but that’s nothing compared to the gathering that’s been described as “Lollapalooza for radiologists”: the annual meeting of the Radiological Society of North America, which kicked off Dec. 1 at Chicago’s McCormick Place.

OK, nobody actually compares RSNA to Lollapalooza. But it is the world’s largest gathering of radiologists, who come to hear from leaders in the field, take in exhibitors’ booths and talk over their profession’s latest trends and technologies. In recent years, that means talk has turned often to artificial intelligence, which is changing the face of medicine in general — enabling speedier test results, clearer diagnostic scans and greater amounts of one-on-one time between doctors and patients. At RSNA 2019, the 105th Scientific Assembly and Annual Meeting, participants will get to check out an expanded AI Showcase that highlights the technology’s implications for their field.

GE Healthcare, whose scientists and physicians will be deeply engaged at this year’s meeting, has done a lot of work at the intersection of healthcare imaging and artificial intelligence. Here’s a selection.

Doctor Data

Dr. Keith Dreyer wears a lot of hats: A radiologist who teaches at Harvard Medical School, he holds a degree in mathematics and a doctorate in computer science, and serves as chief data science officer at Partners HealthCare. It’s in the latter capacity that Dreyer is involved in a collaboration between Partners and GE Healthcare, which in 2017 signed a 10-year agreement to “integrate artificial intelligence into every aspect of the patient journey.” Dreyer spoke to GE Reports at RSNA 2017, laying out the “huge opportunities” AI provides in medical diagnostics, particularly radiology. “I firmly believe that a radiologist plus an AI will beat a radiologist, and will also beat an AI working alone,” Dreyer says. “We have to figure out how to make them work together.”

What’s that look like in practice? Take stroke detection, Dreyer says: “Let’s say we do 200,000 MRI exams of the brain per year and 20,000 are stroke. We can annotate those 20,000, measure the brain lesions caused by the stroke and so on. Next we use the entire 200,000-image set to train the algorithm and use it to identify the type of stroke. When it’s finished, we come back with a test set to see how accurate it was and repeat the process.”

What’s New, Doc?

Adeline Digard knows a thing or two about the advanced technologies hospitals are increasingly incorporating into their diagnostics and treatment — she’s had a hand in creating them. Digard is GE Healthcare’s director of digital product management in Buc, France, where she and her team spend their days thinking about the roles of things like 3D printing, virtual reality and artificial intelligence in healthcare. Not long ago, Digard sat down with GE Reports to explain the importance of the products she’s working on. By way of example, she spoke of how an AI algorithm could help doctors working on spinal health: First the algorithm, sifting through manifold images, learns the correct configuration of vertebrae so it understands what “normal” is. Then it’s introduced to a pathology like scoliosis — and then many, many more, until it has access to a comprehensive database of spinal anatomies and anomalies.

Helping a radiologist reading a CT scan, the AI can then be a tremendous resource: “The machine has already reviewed every orientation of the spine and run through every permutation — much faster than the physician can — and alerted the physician to any abnormalities it’s identified,” Digard said. “It could save the radiologist an enormous amount of time for the software to take care of those universal things. That is, in fact, the entire point of AI — to allow physicians to focus their attention on what is critical for their patients, not spend time on the tedious tasks.”

X-Ray Vision And Then Some

Another place AI can be useful is in detecting collapsed lung, or pneumothorax. Patients with pneumothorax often present with breathing problems, but their symptoms can be similar to those of patients with pneumonia, a heart attack or even a panic attack, so doctors routinely order X-rays to sort out which is which. It can take from two to eight hours for a radiologist to read those scans, though — precious time in which pneumothorax can grow life-threatening. “Currently, 62% of scans are marked ‘STAT’ or for urgent reading, but they aren’t all critical,” says Jie Xue, president and CEO of GE Healthcare’s X-ray division. “This creates a delay in turnaround for truly critical patients, which can be a serious issue.”

There needs to be a way to quickly triage the images, and that’s where AI comes in. GE Healthcare’s Critical Care Suite is a collection of algorithms embedded in a mobile X-ray device that can sort through hundreds of images in minutes and call attention to anything that looks suspicious. The team that developed Critical Care Suite trained the algorithm on chest X-rays culled from hospitals around the world; with the suite recently cleared for use by the FDA, doctors can begin working with the software in pilot tests, and it should start showing up more widely in hospitals in 2020.

Here’s Looking At You, Kid

As a fetal medicine specialist, Dr. Ralf Menkhaus examines ultrasound images for signs of trouble for babies in utero — for instance, spina bifida, a neural tube defect that affects the spinal cord, which doctors can diagnose around the 20th week of pregnancy based on clues like changes in the fetal brain. But fetal brain measurement is an imprecise science in which well-skilled doctors must consider numerous metrics; one sonographer’s assessment can vary from another’s, leading to missed or delayed diagnoses. Menkhaus was delighted, then, to find that he could simplify the process using GE Healthcare’s SonoCNS, an artificial intelligence tool that automates fetal brain assessment and measurements. SonoCNS was trained using Edison, GE Healthcare’s cloud-based app development and data storage and analysis platform. “It’s particularly beneficial for less experienced gynecologists,” Menkhaus says. “Now, with the click of a button, they can have accurate and reliable measurements.”

Sharper Image

Finally, radiologists are getting the kind of image clarity that’s long been available to … gamers? It’s true — and it’s part of the appeal of GE Healthcare’s most advanced radiology ultrasound system, the LOGIQ E10, which is powered by advanced algorithms and the same artificial intelligence technology behind advanced gaming. It can process 10 times more data and generate images faster than previous ultrasound systems. The ability to see an injury or problem spot clearly, for instance, gives patients a measure of security — they can look at their scanned images alongside doctors and find their concerns validated. And for doctors it’s a bonanza. “We don’t even bother marking the body with ink before saphenous vein surgery anymore,” says radiologist John Cronan, who uses the system at Lifespan, Rhode Island’s largest health system. He’s referring here to the practice of marking the location of troublesome varicose veins to guide the surgeon during the operation. “We just send the surgeon a photo.” In other words, it’s a snap.

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A Scanner Smartly: How Artificial Intelligence Is Making Healthcare Imaging Quicker, Kinder And More Efficient

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