Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning

4 April 2022 Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning

Lujia Wang, Zhiyang Zheng, Yi Su, Kewei Chen, David A. Weidman, Teresa Wu, Ben Lo, Fleming Lure, Jing Li

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Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332T (2022) https://doi.org/10.1117/12.2607990
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. Recent advances in tau-positron emission tomography (PET) imaging allow quantitating and mapping out the regional distribution of one important hallmark of AD across the brain. There is a need to develop machine learning (ML) algorithms to interrogate the utility of this new imaging modality. While there are some recent studies showing promise of using ML to differentiate AD patients from normal controls (NC) based on tau-PET images, there is limited work to investigate if tau-PET, with the help of ML, can facilitate predicting the risk of converting to AD while an individual is still at the early Mild Cognitive Impairment (MCI) stage. We developed an early AD risk predictor for subjects with MCI based on tau-PET using Machine Learning (ML). Our ML algorithms achieved good accuracy in predicting the risk of conversion to AD for a given MCI subject. Important features contributing to the prediction are consistent with literature reports of tau susceptible regions. This work demonstrated the feasibility of developing an early AD risk predictor for subjects with MCI based on tau-PET and ML.

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Lujia Wang, Zhiyang Zheng, Yi Su, Kewei Chen, David A. Weidman, Teresa Wu, Ben Lo, Fleming Lure, and Jing Li "Early prediction of the Alzheimer’s disease risk using Tau-PET and machine learning", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332T (4 April 2022); https://doi.org/10.1117/12.2607990

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