Journal article

Publication year: 2022

Journal: Brain Sciences

Volume number: 12

Issue number: 11

eISSN: 2076-3425

Open access status: Gold

DOI Link: https://doi.org/10.3390/brainsci12111522

Publisher: MDPI

Abstract: 
Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate-thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left-right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.

Harvard Citation style: Ruohola, A., Salli, E., Roine, T., Tokola, A., Laine, M., Tikkanen, R., et al. (2022) Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach, Brain Sciences, 12(11), Article 1522. https://doi.org/10.3390/brainsci12111522

APA Citation style: Ruohola, A., Salli, E., Roine, T., Tokola, A., Laine, M., Tikkanen, R., Savolainen, S., & Autti, T. (2022). Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach. Brain Sciences. 12(11), Article 1522. https://doi.org/10.3390/brainsci12111522