BBC Science Focus

AI IS BETTER THAN DOCTORS AT DETECTING BREAST CANCER

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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. 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.

 ??  ?? 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)
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)

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