Warding off needless reads

For Peter Buss Lasborg, the head of Aalborg University Hospital’s Radiology Department, those two technologies—AI and EI—have become inseparable from one another. Given the learnings that led him to this view, his department’s experiences building an AI architecture inside an EI infrastructure may help inform other practices looking to adopt both sources of digital transformation at once.

“What we have is a very straightforward EI infrastructure,” Lasborg tells Radiology Business. “It only involves a DICOM object getting transmitted from the modality to the AI software and then on to the PACS. Radiologists can view the AI read as an image overlay or however they prefer to see it.”

In the case of the first indication for which Aalborg University Hospital has tapped AI, orthopedic fracture detection on X-rays, the algorithm separates patients who may have a fracture from those who definitely don’t. Positive or likely positive findings on AI prompt an application to send the images to a radiologist’s work list for expert interpretation. Negative findings automatically let the care team know that the patient can be treated conservatively and released without direct attention from a physician.

The hospital is currently expanding the EI/AI combo from fracture detection to screening mammography. For this use case, negative screens via AI will obviate the need for a radiologist overread and/or additional imaging.

Next will come pneumothorax detection on plain X-rays. Lasborg notes that projected end-users are open to the idea that AI may only be used for residents and other trainees.

“We’re not sure the AI will really benefit experienced body radiologists because they’re going to read all those images either way, positive or negative,” he says. “We will count on our radiologists to have higher sensitivity and specificity than the pneumothorax AI system. However, for younger doctors, especially in the ER, the AI reads should provide a good educational aid.”
On deck and awaiting its turn is MRI prostate AI, along with other indications yet to be named.

Best yet to come

Looking ahead, Lasborg foresees a future with, for one thing, fewer imaging orders. He hopes the success of the EI/AI combination will embolden referrers to ease off the “just in case” impulse. Instead, in a best-case scenario, most will return to trusting their diagnostic instincts: They’ll order imaging to look for specific ills suspected from patients’ presenting symptoms and spoken complaints.

Lasborg also wants to increasingly tailor Aalborg University Hospital’s AI models to fit North Denmark’s own population.

“Most of the AI systems available today have one input-output mechanism, and that’s all you can get,” he says. “A lot of these models are good enough as starters, but they’re trained on patient data from outside our geographical area. It will be more highly beneficial when we have many more deep learning algorithms trained on—and continuously refined with—datasets from the patients we actually serve.”

High tech, high touch

At Canon Medical Informatics, where Aalborg University Hospital’s EI system was born, Chief Technology Officer Tim Dawson offers insights into the hospital’s success merging new AI architecture into an established EI infrastructure.

“One of the biggest differentiators between enterprise imaging and standard PACS, at least for end-users, is fast and secure collaboration between radiologists and referrers,” says Dawson, who’s been with Canon Medical since 2010. “Without EI you have silos. You have a bunch of departmental systems housing their own copies of data and propagating their own data subsets.”

This presents both clinical and data-security challenges for IT staff. The trick is making sure collaborating physicians can move data without losing any important clinical context or overriding any security measures, Dawson points out.

The key to making it all work is building both EI and AI on new web-based protocols rather than the older DICOM protocols. “This enables us, even on demand, to cache data at the edge rather than centrally,” Dawson explains. “Central caching leaves you open to a major failure if, for example, a natural disaster knocks out local or regional power for an extended period.”

Unstinting focus on care

Dawson has much more to say about the technical ins and outs of EI and AI, but he’s just as keen on the softer skills that, he says, Canon Medical Informatics instills in its people.

“Not long ago I interviewed a technically skilled job candidate for a fairly high-level IT position,” he explains. “I asked why he was looking to stay in healthcare rather than move to another industry. He answered by telling me he’d recently brought in his son for care and had a chance to view the imaging.” When the care team showed him the scans, he saw they were using a PACS he’d had some hand in designing and developing.

The candidate convincingly conveyed to Dawson his excitement over the experience.

Dawson offers: “He’s exactly the kind of person we want to employ at Canon Medical Informatics.”

Lasborg is similarly forward-facing. “We’re always striving to improve care for the patients, families and communities we serve,” he says. “Technology is only part of our continuous improvement mission, and EI and AI are only part of our total technology picture. But they’re a big reason behind our confidence as a leading provider of healthcare services in Denmark.”

The imaging seas are calm. The radiology ship is righted. Aalborg University Hospital is trimmed to maneuver apace for years to come.