Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets

Abstract

Introduction: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets.

Materials and methods: A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence.

Results: The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important.

Conclusion: The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.

Keywords: Deep learning; Explainable artificial intelligence; Magnetic Resonance Imaging; Parkinson’s disease.

Fig. 1

Preprocessing pipeline. The diagram illustrates the preprocessing of the datasets resulting in affinely registered (bottom left), non-linearly registered (bottom center), and Jacobian maps (bottom right). QC = quality control.

Fig. 2

Pipeline for extraction of cortical and subcortical volumes. On the left, the Harvard-Oxford atlases overlayed on top of the ICBM 152 atlas are shown while the transformed Harvard-Oxford segmentations overlayed on the PD25 atlas after registration are shown on the right. After warping the Harvard-Oxford atlases to the subject space, the volumes are computed including the calculation of the intracranial volume derived from the previous brain extraction.

Fig. 3

Average saliency maps of the correctly classified PD patients overlayed on the PD25 atlas. Panel A shows three coronal slices from posterior to anterior, panel B shows three axial slices from inferior to superior, and panel C shows three sagittal slices from left to right. L = left; R = right; A = anterior; P = posterior.

Fig. 4

Volume percentage of the Harvard-Oxford atlases regions covered by the thresholded average saliency map of correctly classified PD patients. Additionally, every region has an mean (standard deviation) intensity value for the corresponding volume percentage covered, also indicated by the color of the corresponding bar. Regions were ranked according to the mean intensity value of the saliency map.