Abstract
Parkinson’s Disease (PD) is a progressive neurological disorder affecting the Basal Ganglia (BG) region in the mid-brain producing degeneration of motor abilities. The severity assessment is generally analyzed through Unified Parkinson’s Disease Rating Scale (UPDRS) as well as the amount changes noticed in the BG size in Positron Emission Tomography (PET) images. Predicting patients’ severity state through the analysis of these symptoms over time remains a challenging task. This paper proposes a Long Short Term Memory (LSTM) model using a newly created dataset in order to predict the next severity stage. The dataset includes the UPDRS scores and the BG size for each patient. This is performed by implementing a new algorithm that focuses on PET images and computes BG size. These computed values were then merged with UPDRS scores in a CSV file. The dataset created is fed into the proposed LSTM model for predicting the next severity stage by analyzing the severity scores over time. The model’s accuracy is assessed through several experiments and reached an accuracy of 84% which outperforms the other state-of-the-art method. These results confirm that our proposal holds great promise in providing a visualization of the next severity stage for all patients which aids physicians in monitoring disease progression and planning efficient treatment.
Supported by Université de Tunis, Institut Supérieur de Gestion de Tunis (ISGT), BESTMOD Laboratory.
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Maalej, Z., Rejab, F.B., Nouira, K. (2023). Deep Learning for Parkinson’s Disease Severity Stage Prediction Using a New Dataset. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_8
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