“It’s imperative that the tumor subtype is known at the time of surgery,” said Jeroen de Ridder, an associate professor in the Center for Molecular Medicine at UMC Utrecht, a Dutch hospital, who helped lead the study. “What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery.”

Their deep learning system, called Sturgeon, was first tested on frozen tumor samples from previous brain cancer operations. It accurately diagnosed 45 of 50 cases within 40 minutes of starting genetic sequencing. In the other five cases, it refrained from offering a diagnosis because the information was unclear.

The system was then tested during 25 live brain surgeries, most of them on children, alongside the standard method of examining tumor samples under a microscope. The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases. It turned around its diagnoses in less than 90 minutes, the study reported — short enough for it to inform decisions during an operation.

Currently, in addition to examining brain tumor samples under a microscope, doctors can send them for more thorough genetic sequencing.

But not every hospital has access to that technology. And even for those that do, it can take several weeks to receive results, said Dr. Alan Cohen, the director of the Johns Hopkins Division of Pediatric Neurosurgery and a cancer specialist.