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 Shrot^2,3, Moti Salti^4,5, Hayit Greenspan¹, and Noam Ben-Eliezer^6,7

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

Synopsis

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.

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