Projects

Developing an AI System for Brain MRI Diagnoses

Principal author

Andreas Rauschecker, MD, PhD
Assistant Professor

Contributors

Andreas Rauschecker, MD, PhD, a clinical fellow in neuroradiology, is developing an AI system for probabilistic brain MRI diagnoses.  This system computationally models a neuroradiologist’s process of image interpretation by using a convolutional neural network for detection of imaging abnormalities, image processing for quantitative descriptions of these abnormalities in terms of signal, location, and volumetric features, and a probabilistic integration of these derived features with clinical features in the form of Bayesian inference, ultimately arriving at a probability-ranked differential diagnosis. This set of methods has been applied to 50 unique neurological diseases, where the AI system has performed with promising results. This work was supported by an RSNA Resident Grant and by the NIH T-32 training grants at UCSF and at the University of Pennsylvania Radiology Departments.

 

Lesion Detection / Segmentation
Goals
Methodology
Desired clinical outcomes