Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks

TL;DR

This paper develops convolutional neural network models to accurately detect early Parkinson's disease from SPECT imaging, addressing diagnostic challenges and aiding clinical decision-making.

Contribution

Introduces CNN-based models utilizing SPECT features for early PD detection, improving diagnostic accuracy over existing methods.

Findings

High accuracy in distinguishing early PD from normal scans

Effective identification of SWEDD subjects

Potential to assist clinicians in early diagnosis

Abstract

Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective treatment regimens and patient management. For instance, a group of subjects termed SWEDD who are clinically diagnosed as PD, but show normal Single Photon Emission Computed Tomography (SPECT) scans, change their diagnosis as non-PD after few years of follow up, and in the meantime, they are treated with PD medications which do more harm than good. In this work, machine learning models are developed using features from SPECT images to detect early PD and SWEDD subjects from normal. These models were observed to perform with high accuracy. It is inferred from the study that these diagnostic models carry potential to help PD clinicians in the diagnostic process