A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

TL;DR

This paper presents an automatic AI-based framework that integrates multi-modality imaging data to diagnose Parkinson's disease with high accuracy, reducing reliance on expert interpretation.

Contribution

It introduces an end-to-end multi-modality diagnosis framework combining segmentation, registration, feature extraction, and machine learning for PD detection.

Findings

Achieved 100% accuracy in PD/NL classification.

Multi-modality images outperform single modality in diagnosis.

Automatic segmentation matches manual segmentation performance.

Abstract

Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis.