by
John R. Fischer, Senior Reporter | October 23, 2019
The algorithm also included information that physicians needed to determine the most optimal treatment, and made all its findings with an acceptable level of false positives. This reduced the amount of time needed to review results.
“We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect,” said Yuh in a statement. “The performance bar is high for this application, due to the potential consequences of a missed abnormality, and people won’t tolerate less than human performance or accuracy.”
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The authors are currently evaluating the algorithm’s use in assessing CT scans from trauma centers nationwide, as part of a research study headed by Dr. Geoffrey Manley, professor and vice chair of neurosurgery at UCSF.
Funding was provided by the California Initiative to Advance Precision Medicine (California Governor’s Office of Planning and Research) and Swiss National Science Foundation Early Postdoc. Mobility Fellowship 165245. Computing Time was facilitated by Amazon Web Services.
The findings were published in
Proceedings of the National Academy of Sciences (PNAS).
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