Rationale and Objectives
Misdiagnosis of intracranial hemorrhage (ICH) can adversely impact patient outcomes.
The increasing workload on the radiologists may increase the chance of error and compromise
the quality of care provided by the radiologists.
Materials and Methods
We used an FDA approved artificial intelligence (AI) solution based on a convolutional
neural network to assess the prevalence of ICH in scans, which were reported as negative
for ICH. We retrospectively applied the AI solution to all consecutive noncontrast
computed tomography (CT) head scans performed at eight imaging sites affiliated to
our institution.
Results
In the 6565 noncontrast CT head scans, which met the inclusion criteria, 5585 scans
were reported to have no ICH (“negative-by-report” cases). We applied AI solution
to these “negative-by-report” cases. AI solution suggested there were ICH in 28 of
these scans (“negative-by-report” and “positive-by-AI solution”). After consensus
review by three neuroradiologists, 16 of these scans were found to have ICH, which
was not reported (missed diagnosis by radiologists), with a false-negative rate of
radiologists for ICH detection at 1.6%. Most commonly missed ICH was overlying the
cerebral convexity and in the parafalcine regions.
Conclusion
Our study demonstrates that an AI solution can help radiologists to diagnose ICH and
thus decrease the error rate. AI solution can serve as a prospective peer review tool
for non-contrast head CT scans to identify ICH and thus minimize false negatives.
Key Words
Abbreviations:
ICH (Intracranial Hemorrhage), CNN (Convolutional Neural Hemorrhage), AI (Artificial Intelligence), NLP (Natural Language Processing)To read this article in full you will need to make a payment
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Article Info
Publication History
Published online: February 24, 2020
Accepted:
January 29,
2020
Received in revised form:
January 27,
2020
Received:
October 16,
2019
Identification
Copyright
© 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
