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Coronavirus pandemic
ChinaPeople & Culture

Chinese supercomputer and American doctors clash over AI coronavirus diagnosis of young US vaper

  • China’s Tianhe-1 machine says white patches detected on lungs suggest probable Covid-19
  • But North Carolina doctor says five patients with similar symptoms were not evidence of an earlier outbreak

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A patient at a US hospital in July or August last year may have had coronavirus, concluded a Chinese supercomputer given the task of diagnosing Covid-19. US doctors said the symptoms were possibly associated with vaping. Photo: Nora Tam
Stephen Chen
A supercomputer in China found that a young vaper in hospital for a “mysterious pneumonia” in the United States last summer could be a victim of coronavirus.
The Tianhe-1 machine detected distinguished features – suggesting high probability of Covid-19 – in the CAT image of a patient who became severely ill between July and August in North Carolina, according to a diagnosis report seen by the South China Morning Post. The machine is at the National Supercomputer Centre in Tianjin.
The Tianhe-1 supercomputer AI diagnosis report for a North Carolina patient with mysterious lung infection. Photo: National Supercomputing Centre in Tianjin
The Tianhe-1 supercomputer AI diagnosis report for a North Carolina patient with mysterious lung infection. Photo: National Supercomputing Centre in Tianjin
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The features included white patches encroaching on the lower parts of both lungs, a phenomenon raising the eyebrows of some radiologists at the early stage of the outbreak because it was rarely seen in other types of pneumonia.

“The image shows features of Covid-19, and a comprehensive diagnosis is recommended in combination with epidemiological information and other clinical characteristics,” said the report generated by artificial intelligence.

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The patient was one of five people who showed up at the WakeMed Hospital in Raleigh, North Carolina, at about the same time, with acute lung injuries, according to information on the website of the US Centres for Disease Control and Prevention.

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