Deep Learning used by Johns Hopkins Researchers to detect Pancreatic Cancer Early

August 20, 2018  Source: Healthcare IT News 208

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Elliot K. Fishman, MD, professor of radiology, surgery, oncology and urology at Johns Hopkins Hospital seeks the help of GPU-accelerated deep learning artificial intelligence to detect pancreatic cancer early which is nearly impossible for humans alone.

His work is focused on spotting the earliest indicators of cancer by guiding deep learning algorithms to detect slightest changes in the texture of the tissue of the pancreas and adjacent organs. Fishman approximates that almost a third of the cases he receives could have been diagnosed 4 to 12 months earlier with deep learning detection.

"The major treatment for pancreatic cancer for cure is surgery, but unfortunately, because of late detection, no more than 15-20 percent of patients at the time of presentation are surgical candidates," said Fishman.

"Sometimes it is indeed simply by a retrospectoscope that we see the findings and sometimes it was simply just not seen by the initial interpreter," Fishman explained. "The goal of using GPU-accelerated deep learning would be to optimize lesion detection so that you can detect every lesion that is present at the earliest time."

He thinks that the one thing Johns Hopkins has to rely on the supercomputers for is to discover and identify even fine changes in texture and pattern than just lumps.

"Our group has two NVIDIA DGX-1 supercomputers," he explained. "The DGX-1 is the state of the art in AI and is necessary for our work. It allows us to review and study the results of hundreds of cases simultaneously and to be able to change parameters, develop algorithms that would be otherwise impossible.”

"It is these algorithms that allow us to optimize the detection of tumor and to eventually be able to define specificity of tumor type and hopefully also allow us to determine better management strategies," he added.

By Ddu
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