Predicting Parkinson's disease evolution using deep learning

• URL: http://arxiv.org/abs/2312.17290v2
• Date: Fri, 5 Jan 2024 09:36:36 GMT
• Title: Predicting Parkinson's disease evolution using deep learning
• Authors: Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica
• Abstract summary: Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. There is not a single blood test or biomarker available to diagnose Parkinson's disease. No AI tools have been designed to identify the stage of progression.
• Score: 1.4610685586329806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. The disease is manifested by a drop in dopamine production, symptoms are cognitive and behavioural and include a wide range of personality changes, depressive disorders, memory problems, and emotional dysregulation, which can occur as the disease progresses. Early diagnosis and accurate staging of the disease are essential to apply the appropriate therapeutic approaches to slow cognitive and motor decline. Currently, there is not a single blood test or biomarker available to diagnose Parkinson's disease. Magnetic resonance imaging has been used for the past three decades to diagnose and distinguish between PD and other neurological conditions. However, in recent years new possibilities have arisen: several AI algorithms have been developed to increase the precision and accuracy of differential diagnosis of PD at an early stage. To our knowledge, no AI tools have been designed to identify the stage of progression. This paper aims to fill this gap. Using the "Parkinson's Progression Markers Initiative" dataset, which reports the patient's MRI and an indication of the disease stage, we developed a model to identify the level of progression. The images and the associated scores were used for training and assessing different deep-learning models. Our analysis distinguished four distinct disease progression levels based on a standard scale (Hoehn and Yah scale). The final architecture consists of the cascading of a 3DCNN network, adopted to reduce and extract the spatial characteristics of the RMI for efficient training of the successive LSTM layers, aiming at modelling the temporal dependencies among the data. Our results show that the proposed 3DCNN + LSTM model achieves state-of-the-art results by classifying the elements with 91.90\% as macro averaged OVR AUC on four classes

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