NYU uses Google's AI to identify lung cancer

Researchers at New York University in New York City trained an artificial intelligence algorithm developed by Google to distinguish between two common types of lung cancer, Wired reports.

The researchers used Google's Inception v3 — an open-source deep learning algorithm — and trained it to differentiate between cancerous and healthy tissue images using thousands of images collected by The Cancer Genome Atlas, a public database of patient tissue samples. They also taught it to distinguish between two types of lung cancer, which are similar in appearance: adenocarcinoma and squamous cell carcinoma.

The researchers tested the retrained deep learning algorithm on image samples from cancer patients at NYU, and found it accurately diagnosed patients 83 to 97 percent of the time — only using images of the tumor, and without including information from sequencing or other tests.

"I thought the real novelty would be not just to show the AI is as good as humans, but to have it provide insights a human expert would not be able to," Aristotelis Tsirigos, PhD, a pathologist at NYU School of Medicine and a lead author on the new study, told Wired. "These cancer-driving mutations appear to have microscopic effects that the algorithm can detect."

The researchers plan to continue to train the algorithm with data from additional sources, and are considering seeking FDA approval.

To read the study in Nature Medicine, click here.

More articles on artificial intelligence:
IBM says its new services 'open the black box of AI'
Google Cloud taps Carnegie Mellon professor to lead AI
Mayo Clinic, Mass General, Nvidia researchers use AI to create 'synthetic MRIs'

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