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J Neurosci Methods. 2018 Jul 15;305:105-116. doi: 10.1016/j.jneumeth.2018.05.009. Epub 2018 May 22.

Determination of Imaging Biomarkers to Decipher Disease Trajectories and Differential Diagnosis of Neurodegenerative Diseases (DIsease TreND).

Author information

1
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore; Department of Radiology, Dalio Institute of Cardiovascular Imaging, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, United States. Electronic address: gus2011@med.cornell.edu.
2
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore; Residential College 4, 8 College Avenue West, #02-16W, Education Resource Centre, Singapore 138608, Singapore. Electronic address: laksh@nus.edu.sg.
3
Department of Neurology, National University Health System, National University of Singapore, Singapore. Electronic address: mdcelch@nus.edu.sg.

Abstract

BACKGROUND:

Understanding disease progression of neurodegenerative diseases (NDs) is important for better prognosis and decisions on the appropriate course of treatment to slow down the disease progression.

NEW METHOD:

We present here an innovative machine learning framework capable of (1) indicating the trajectory of disease progression by identifying relevant imaging biomarkers and (2) automated disease diagnosis. Self-Organizing Maps (SOM) have been used for data dimensionality reduction and to reveal potentially useful disease-specific biomarkers, regions of interest (ROIs). These ROIs have been used for automated disease diagnosis using Least Square Support Vector Machines (LS-SVM) and to delineate disease progression.

RESULTS:

A multi-site, multi-scanner dataset containing 1316 MRIs was obtained from ADNI3 and PPMI. Identified biomarkers have been used to decipher (1) trajectory of disease progression and (2) identify clinically relevant ROIs. Furthermore, we have obtained a classification accuracy of 94.29 ± 0.08% and 95.37 ± 0.02% for distinguishing AD and PD from HC subjects respectively.

COMPARISON WITH OTHER EXISTING METHODS:

The goal of this study was fundamentally different from other machine learning based studies for automated disease diagnosis. We aimed to develop a method that has two-fold benefits (1) It can be used to understand pathology of neurodegenerative diseases and (2) It also achieves automated disease diagnosis.

CONCLUSIONS:

In the absence of established disease biomarkers, clinical diagnosis is heavily prone to misdiagnosis. Being clinically relevant and readily adaptable in the current clinical settings, the developed framework could be a stepping stone to make machine learning based Clinical Decision Support System (CDSS) for neurodegenerative disease diagnosis a reality.

KEYWORDS:

Computer-assisted diagnosis; Differential diagnosis; Imaging biomarkers; Machine learning; Neurodegenerative disease diagnosis; Unsupervised learning

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