A new deep learning model designed to help radiologists read noncontrast CT exams saved time and improved accuracy, according to new findings published in European Radiology.
The study's authors focused on 32 radiologists who evaluated 2,848 non-contrast CT brain scans with and without the assistance of advanced artificial intelligence (AI) technology developed by annalise.ai. Radiologists assessed reports and diagnosed patients without the aid of the Annalise Enterprise CTB system, then later the same radiologists conducted similar readings with the assistance of the AI technology.
Non-contrast CT brain scans are widely used for the detection of intracranial pathology, but their interpretation can be subject to errors. The study aimed to see how useful AI technology can be in providing a second set of eyes, to measure how well the system can improve the diagnostic accuracy of trained radiologists.
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