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At Wake Radiology in North Carolina, roughly 50 doctors scrutinize x-rays and other images for local medical providers. Within a few weeks, they should start to get help on some lung CT scans from machine-learning algorithms that highlight potentially cancerous tissue nodules. Although Wake is based in a region known as the Research Triangle, for its intensity of high-tech R&D, the lung-reading software hails from elsewhere—China.
Infervision, a four-year-old Beijing startup, has amassed more than a million scans from Chinese hospitals that it’s using to train and test algorithms. Gathering medical data is much easier for Chinese companies than for their US counterparts, because patient populations are larger and the burden of privacy regulations smaller.
US - Hospitals - Processes - Time - Access
“In the US, particularly for big academic hospitals, you have to go through so many processes and it can take a really long time to access data,” says Yufeng Deng, Infervision’s chief scientist. Chinese institutions do take steps to protect patient privacy, such as anonymizing records used in research, he says, but they are not bound by as many rules and regulatory processes. “In China it’s less well-defined—if the hospital’s information people say it’s fine, you can do it,” Deng says.
To create its algorithm to identify lung nodules, Infervision gathered more than 400,000 lung scans from Chinese partners, such as leading Beijing research center Peking Union Medical College Hospital. Over two years, it then sent each image for review by three radiologists at its Beijing office. Their annotations created the feedstock to train and test image-processing algorithms, in the same way internet companies train systems to recognize cats, dogs, and people. Infervision has published peer reviewed studies in Chinese and US journals on its algorithms' performance. In a pilot at Shanghai Changzheng Hospital, two radiologists found that Infervision’s product could dramatically boost their ability to annotate lung...
(Excerpt) Read more at: WIRED
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