Deep Learning-Based Multimodal Approach for Improved Classification and Prediction of Parkinson's Diseases

Abstract

Parkinson disease (PD) is an intractable problem, because of its clinical heterogeneous complexity and drawbacks of the available standard means of diagnosis. Timely recognition, proper identification and effective prediction of progression of the disease are critical towards enhancing the treatment outcome. The current study suggests a new AI-based approach that exploits multimodal inputs to increase the accuracy of diagnosis, classification, and prediction of PD. This framework combines clinical, neuroimaging, genetic and behavioral information and thus it will be possible to gain an in-depth picture of the progression of the disease. It integrates the methods of deep learning, and the specifics of the methodology are the use of the technique of convolutional neural networks (CNNs) to analyze the images of neuroimaging data and the technology of long short-term memory (LSTM) networks to process the temporal clinical data. Moreover, feature selection and dimensionality reduction methods are also applied to load genetic data into the learning process to guarantee optimal extraction of predictive features. The multimodality data processing capability of the hybrid model allows the software to recognize less discernible patterns that portray early-stage PD and model the disease more accurately than the available models. The framework was then validated using several publicly accessible PD datasets that proved that the accuracy was superior, as well as sensitivity and specificity. This paper draws attention to the prospects of AI in changing the horizon of PD diagnostics and personalized medicine, providing a trustworthy instrument that can be used by a clinician to base his / her decisions on the data in the treatment of the disease Parkinson.