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    October 1, 2017

    Artificial Intelligence Aids the Accuracy and Efficiency of the Interpreting Radiologist

    Bridging the disciplines of neuroscience, computer science, medical imaging and informatics, 2017 RSNA Research Resident Grant recipient Andreas Rauschecker, MD, PhD, is pursuing an approach that combines advances in the fields of image processing and artificial intelligence (AI) to perform automated neuroradiologic diagnosis.

    “Proof-of-concept development will focus on image-based feature quantification algorithms for lesion detection and characterization in two traditionally difficult sets of neuroimaging disease types: those involving the basal ganglia and those involving the white matter,” said Dr. Rauschecker, a resident at the University of Pennsylvania, Philadelphia.

    “These general methods, when established, may apply to other pathologies and to other organ systems in the future, providing a framework for AI algorithms that can transform images to differential diagnosis for both common and rare diseases. Our hope is that these types of algorithms can greatly aid both the accuracy and efficiency of the interpreting radiologist.”

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    Andreas Rauschecker, MD, PhD

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