With Triage System, Companies See Toehold for A.I. in Radiology

This story is part of an ongoing Xconomy series on A.I. in healthcare. Other stories cover big-company efforts, a genomics hackathon, the impact on doctors and patients, and business models of A.I. in healthcare.

Years ago, radiology underwent a radical transformation with the shift from film to widespread use of digital image displays. That set the stage for the kinds of artificial intelligence technologies that promise—or threaten, depending on your perspective—to upend the profession again today.

As with any new technology that would impact the healthcare industry, even a relatively small corner of it, a major challenge persists: How can it find a way through the medical bureaucracies and into the harried routines of practicing healthcare providers?

That’s where a company called Clario sees its opening. Hundreds of radiologists already use the Seattle firm’s software to manage work in their increasingly large practice groups. Now, Clario is teaming with CuraCloud, an under-the-radar Seattle startup building image analysis, genomics, and natural language processing technologies, to develop an A.I. tool for radiologists that would automatically identify life-or-death radiographic exams and move them to the top of the radiologist’s queue.

“What our A.I. can do is escalate the priority to ‘stat’: look at this exam next,” says Ed Butler, vice president of marketing at CuraCloud. “It shortens the time between [taking the scan] and the radiologist actually looking at it, based on the severity of the situation.”

The companies’ partnership illustrates a possible pathway to market for the multitude of startups and corporations looking to bring A.I. to the front lines of healthcare.

CuraCloud vice president Ed Butler.

Many companies in A.I. are working on “really intellectually interesting problems,” Butler says. “But getting it actually into the workflow at the right time has been one of the toughest nuts to crack.”

Clario, founded in 2008, solves a problem that emerged after the physical X-ray films that used to be carried by hand—their folders covered with notes and special instructions—were replaced with digital image files. Hospitals adopted picture, archive, and communications systems (PACS), untethering radiologists from the physical location of the scanning equipment and changing old ways of working.

Clario’s software enables some of the workflows from the old world to continue in a digital format, allowing radiology practice groups that might be working with several hospitals and health systems to manage and prioritize incoming scans, assign them to specific radiologists, and communicate with the referring physicians.

As private radiology practices have grown larger, the ability to efficiently manage incoming work has become essential. “They have compensation that’s centered around how productive they are,” says Clario co-founder and CEO Chris Wood. “So we were able to sell to that market initially because we had a direct impact on the bottom line of the practice.”

Clario counts 25 private practice customers with some 1,200 radiologists using the company’s software to manage their daily work.

Today, each practice using Clario’s software sets its own rules to determine which incoming scan should be read next. But the A.I. technology CuraCloud is developing would automate that prioritization for certain scans that could contain life-or-death information about a patient.

A cranial scan with a possible brain hemorrhage would usually be sent “stat” by a referring physician to a radiologist, but it doesn’t always happen. And in some busy practices and hospitals, that life-critical scan could fall through the cracks and languish for hours or even a day on the radiologist’s digital pile of scans to be read.

For scans that meet certain criteria, the Clario system would call CuraCloud’s A.I. module, triggering an automated evaluation of the incoming image file. (The module would likely reside within the radiology practice, rather than in the cloud, because of the large file sizes and need for rapid evaluation, though it could also be deployed in the cloud, Butler says. The major public cloud providers are in a race to add more capable machine learning applications to their menu of services.)

Butler says CuraCloud’s team of computer scientists and diagnostic imaging equipment experts have built a deep neural network—a type of machine-learning algorithm—that can classify radiographic images. Each A.I. module is tuned to detect a specific condition—intracranial hemorrhage, for example.

CuraCloud has had no trouble obtaining data to train its triage algorithms, Butler says. There are public-domain datasets of radiologic images that can be used, as well as “any number of organizations that are very interested in collaborating with us and sharing their data in a controlled way, of course,” he says.

That said, there is a data land-grab going on in the business of A.I. for healthcare, with lots of questions swirling around.

“It comes down to who has access to it, who owns it, and who has rights to it—and can you use it, under what conditions can you use it?” Wood says, adding that definitive answers are few.

Companies like Clario that are already working with care providers—its customers process 1.3 million exams a month, creating a trove of tagged training data—would seem to be at an advantage. One challenge for the industry is establishing the right legal and business permissions to use the data, but once that’s resolved, the data will flow, allowing machine learning applications to continually improve, Wood says.

Another challenge is regulatory compliance. Digital innovation in healthcare, particularly something that impacts the use of regulated medical devices such as radiographic scanners, must first … Next Page »

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