Meeting Banner
Abstract #2794

Psychophysical evaluation of radiologic vs. deep-learning based identification of multiple sclerosis brain lesions

Chen Solomon1, Omer Shmueli1, Tamar Blumenfeld-Katzir1, Dvir Radunsky1, Noam Omer1, Neta Stern1, Shai Shrot2,3, Moti Salti4,5, Hayit Greenspan1, and Noam Ben-Eliezer6,7
1Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel, 3Tel Aviv University, Tel Aviv, Israel, 4Brain Imaging Research Center, Soroka Medical Center, Beer Sheva, Israel, 5University Medical Center, Ben Gurion University, Beer Sheva, Israel, 6Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States, 7Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel


Computer assisted detection (CAD) of pathology in MRI scans may provide higher sensitivity to tissue changes. We present rigorous comparison of CAD vs. conventional radiologic evaluation of multiple sclerosis (MS) lesions. A psychophysical experiment was performed, where radiologists and a deep neural-network were asked to detect artificial MS lesions, synthetically simulated on T2-weighted FLAIR images, and at 8 levels of severity. Odds ratio analysis indicated that the human vision is less sensitive to low-severity lesions. This suggests that CAD can improve early detection of tissue abnormalities in the brain.

This abstract and the presentation materials are available to members only; a login is required.

Join Here