Thought Leaders Seminar, Business Analytics: Teresa Wu

Apr 21, 2023

01:00 PM - 02:00 PM

Pappajohn Business Building, C125

21 East Market Street, Iowa City, IA 52245

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Topic: Deep Learning to identify MRI signatures along the age continuum using Brain Age.

Imaging biomarkers are being increasingly applied for early diagnosis and staging of disease in humans. Developing imaging biomarkers requires advances in both image acquisition and analysis. In recent years, deep learning has rapidly dominated the computer vision field with advances also diffusing into the medical field. The objective of this study is to assess a deep learning approach to characterizing brain age signatures using MRI from cognitively normal subjects and explore its potential as biomarkers in neurodegenerative disease (e.g., Alzheimer’s disease) diagnosis. Two 3D deep ResNet-18 models were implemented to predict the chronological age of the healthy subjects. Both models were trained on 7372 T1 MRI from a combined lifespan cohort of 5848 cognitively normal participants (age: 8- 95 yrs). The first ResNet model was trained to regress the chronological age of participants on 3D MRI scans with a linear layer as the last layer. Different from that, we trained the other ResNet model for a multi-class classification where chronological age values were discretized into 86 classes and the last layer was a fully connected layer. The regression model achieved an MAE=3.76 years whereas the classification model achieved an MAE=2.65 years on same lifespan cohort. Both ResNet models were able to achieve state-of-art performance in predicting brain age. Using a mean-variance loss and translating the age prediction task into multi-class classification, the performance of brain age prediction was improved. We further derived the brain age signatures from ADNI (Alzheimer’s Disease Neuroimaging Initiative) cohort and observed group differences between Mild Cognitive Impairment (MCI) and Healthy Control (p<0.05). This supported our hypothesis that brain signatures have the potential to support neurodegenerative disease diagnosis.  

Teresa Wu is a Professor from School of Computer and Augmented Intelligence (SCAI), Arizona State University and an adjunct Professor of Radiology in College of Medicine, Mayo Clinic. Her current research interests include imaging informatics and clinical decision support. Professor Wu has published ~140 journal articles in journals such as NeuroImage, NeuroImaging: Clinical, Brain Communication, IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Science. Professor Wu is the founding Director of the ASU-Mayo Center for Innovative Imaging. She received numerous awards including NSF CAREER award (2003), AFOSR Summer Faculty Fellow (2010, 2011), IBM Faculty Award (2017), IISE Fellow (2020) and ASU PLuS Fellow on Global Health (2016-2020). She was a former Editor-in-Chief for IISE Transactions on Healthcare Systems Engineering (2016-2020).  

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