Hand Drawing Image based Causal Representation Learning for Robust Parkinson's Disease Feature Extraction and Detection

Abstract

Being an irreversible disorder regarding the human motor-system, Parkinsons Disease(PD) has been a threat to many neurological patients, especially due to its severity in pain and muscle control restriction. As PD has no significant cure or treatments to this day, early diagnosis, or detections of PD within potential patients is a crucial task to maximize the effect of mediations which are implemented to achieve temporal prohibition of motor failure progression. In recent research, alongside conventional diagnosis methods based on neurological examinations or MRI based brain imaging, use of deep learning based artificial intelligence models, such as ResNet, are repeatedly reported to have significant progress in detecting PD in early stages with high performance. Based on current success, this research attempted to further enhance AI-driven PD diagnosis by developing a deep learning based causal representation learning framework that extracts only highly robust PD features from simple hand drawings. Specifically, convolutional VAE based reconstruction and information theory based weakly supervised learning were linked with causal representation learning methods to distinguish significant PD features from geometrical features within hand drawing images. Not only aiding conventional tests for PD diagnosis, but also giving reliable representations of PD features such as tremor and rigidity, developed framework was found to achieve high performance in both retrieving latent factors of PD in images and predicting PD diagnosis results.