1 results match your criteria: "USA. From the David Geffen School of Medicine (K.N.)[Affiliation]"
Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.
AJNR Am J Neuroradiol
September 2024
From the Departments of Clinical Affairs and Research & Development (A.A., M.T., S.Q., S.F., C.A., Y.C.), Avicenna.AI, La Ciotat, France. From the Department of Engineering (A.D.), Amalgamated Vision, LLC, Brentwood, TN, USA. From the Department of Radiological Sciences (P.D.C., D.S.C., D.F.), University of California Irvine, Orange, CA, USA. From the Center for Artificial Intelligence in Diagnostic Medicine (P.D.C., D.S.C.), University of California Irvine, Orange, CA, USA. From the David Geffen School of Medicine (K.N.), UCLA, Los Angeles, CA, USA. From Advance Research Associates (P.S.) Santa Clara, CA, USA.
Article Synopsis
- The ASPECTS criteria for assessing acute ischemic stroke is complex, leading to variability in interpretations among physicians, prompting a study to evaluate the impact of a deep learning (DL) algorithm on clinicians' performance.
- A total of 200 non-contrast CT scans were reviewed by various clinicians with and without the support of the CINA-ASPECTS algorithm, which automates the ASPECTS assessment.
- Results showed that the software improved accuracy and consistency in evaluations while also reducing the time needed for assessment, indicating its potential to enhance clinical decision-making in stroke treatment.