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Abstract #0575

Impact of Defacement on Deep Learning-based Amyloid Prediction with Multi-contrast MRI

Donghoon Kim1, Jon André Ottessen2, Ashwin Kumar1, Brandon C. Ho1, Christina B. Young3, Elizabeth Mormino3, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2University of Oslo, Oslo, Norway, 3Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, Defacement

Motivation: Defacement has become standard in neuroimaging to protect privacy but may inadvertently remove valuable information for predicting amyloid PET positivity from MRI scans.

Goal(s): To assess whether defacement impacts deep learning models' ability to predict amyloid positivity from MRI data.

Approach: Using non-processed, defaced, and skull-stripped T1w and T2-FLAIR images, we trained EfficientNet-B1 and SEResNet50 models to predict amyloid PET positivity, comparing performance across different inputs.

Results: Skull-stripped images achieved the highest accuracy, outperforming both non-processed and defaced images. While facial information may not enhance predictions, defacement reduced accuracy, as non-defaced images outperformed defaced ones. This warrants further exploration with advanced models.

Impact: Defacing MRI scans for privacy inadvertently removes information that may aid amyloid PET positivity prediction, as shown by decreased model performance on defaced images compared to non-defaced ones.

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