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Combined Use of Gray Matter Volume and Quantitative Susceptibility Mapping to Predict Early Alzheimers Disease Using a Machine Learning-Based Optimized Combination-Feature Set

HK Kim1 , HY Rhee2 , CW Ryu3 ,GH Jahng3*, (1) Radiology, Kyung Hee University Hospital, Seoul,Korea ,(2) Neurology, Kyung Hee University Hospital at Gangdong, Seoul,Korea ,(3) Radiology, Kyung Hee University Hospital at Gangdong, Seoul,Korea


(Sunday, 7/14/2019) 4:00 PM - 5:00 PM

Room: Exhibit Hall | Forum 1

Purpose: To investigate the approach of classification and prediction methods using the machine learning (ML)-based optimized combination-feature (OCF) set on gray matter volume (GMV) and quantitative susceptibility mapping (QSM) in elderly subjects with a cognitive normal (CN) profile, those with amnestic mild cognitive impairment (aMCI), and mild and moderate Alzheimer’s disease (AD) patients.

Methods: GMV and QSM in the brain were calculated from isotropic 3D T1-weighted images (MPRAGE sequence) and 3D multi-echo gradient-echo (fast field-echo (FFE) sequence) images, respectively, in 19 CN subjects, 19 aMCI subjects, and 19 AD patients. To differentiate the three subject groups with the optimized combination-feature (OCF) set, the SVM kernel classifiers with three different kernels were conducted with GMV and QSM values. The predictive analysis was performed in the classification between the aMCI stage and the CN profile using the following three regression models for prediction: RQ, SE, and EXP GPR models. The regression performance was assessed using the root mean square error (RMSE).

Results: The highest accuracies were shown for the combination of GMVs and QSMs data using the 2nd SVM classifier in the group classification between CN and aMCI subjects (AUC = 0.94) and in the group classification of CN and AD subjects (AUC = 0.99). To distinguish aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data showed results most similar (RMSE = 0.371) to those obtained using clinical data (RMSE = 0.319).

Conclusion: The ML-based OCF setting technique with GMVs (the hippocampus and the entorhinal cortex) and QSMs (the hippocampus and the pulvinar) was shown to effectively classify the subject group and predict the aMCI stage, indicating that the OCF set with GMV and susceptibility can be used for personalized analysis or as a diagnostic aid program.

Funding Support, Disclosures, and Conflict of Interest: Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B03930720, GHJ)and the Convergence of Conventional Medicine and Traditional Korean Medicine R&D program funded by the Ministry of Health & Welfare through the Korea Health Industry Development Institute (KHIDI) (HI16C2352, GHJ)


MRI, Quantitative Imaging, Image Analysis


IM- MRI : Machine learning, computer vision

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