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Abstract #4531

Interpretable neural network based on T1-weighted MRI for detecting Parkinson's disease

Chen Yang Cao1, Lu Han2, and Zhong Zheng Jia1
1Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong 226001, China, 2Philips Healthcare, Shanghai 200072, China

Synopsis

Keywords: Parkinson's Disease, Parkinson's Disease

Motivation: Applying radiological techniques for early diagnosis of Parkinson's disease (PD) is crucial for prognosis of patients. A simple, accurate technique with high patient compliance is still lacking.

Goal(s): To explore the feasibility of using visual neural networks based on T1-weighted images for detecting PD.

Approach: Patients with PD and healthy controls (HCs) underwent T1-weighted MRI scans. A convolutional neural network (CNN) architecture was developed. Interpretable maps were also drawn to investigate the efficiency of computerized neural networks.

Results: The CNN model achieved achieved good classification performance.Interpretable maps highlighted the critical regions for detecting PD.

Impact: CNN based on T1-weighted imaging is a reliable and accurate diagnostic tool for detecting PD. Interpretable maps improve the explainability of classification results in clinical applications.

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Keywords