The AI was shown a microscope slide containing lymph nodes (left), and it was able to correctly identify the tumorous region by highlighting it  in red (right) © Naval Medical Center San Diego

Google AI better than doctors at detecting breast cancer

Google’s deep learning AI called LYNA able to correctly identify tumorous regions in lymph nodes 99 per cent of the time.

Google’s deep learning AI has proven that it is more accurate than pathologists at detecting breast cancer that has spread to a patient’s lymph nodes.

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The presence or absence of these ‘nodal metastases’ influence a patient’s prognosis and treatment plan, so accurate and fast detection is important. But in some cases, only 38 per cent of small metastases are picked up by pathologists when samples are reviewed under time constraints, and right now, that pathologist’s examination is the gold standard in diagnosis of nodal metastases.

Google customised one of its ‘off-the-shelf’ deep learning approaches, calling it LYNA (LYmph Node Assistant). Among other things, it was taught to examine the images at different magnifications, similar to how a pathologist examines slides.

The algorithm’s first test showed that LYNA was able to correctly distinguish a slide with cancer from a slide without 99 per cent of the time, even when the regions were too small to be detected by pathologists.

In the second, six pathologists completed a diagnostic test with and without LYNA’s assistance. With LYNA’s help, the doctors found it ‘easier’ to detect small metastases, and on average the task took half as long. Pathologists working with LYNA’s assistance were more accurate than both unassisted pathologists and the LYNA algorithm working alone.

Google’s researchers suggest that algorithms like LYNA could help with these identification tasks to allow more time for pathologists to work on more complex diagnoses. But for now, further testing is needed to determine if LYNA will work in real-life settings, which could involve a wider range of samples from different sites in the body.

Medicine gets personal © Tang Yau Hoong

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