Early Parkinson's Disease Diagnosis Using Multi-Modal CASENet CNN-LSTM

Introduction

Utilizing handwriting and voice documents as potential indicators, deep learning aids in the diagnosis of Parkinson's disease. Pen pressure, pace, as well as stroke patterns are among the fine motor skill details that deep learning algorithms record for handwriting analysis. The models identify variations in pitch, rhythm, as well as other acoustic characteristics associated with Parkinson's symptoms in voice recordings (Mohaghegh et al. 2021). The accuracy of diagnosis is improved by combining all of these multimodal sources of data. Deep learning models that have been trained on a variety of datasets detect minute patterns that point to the illness, enabling early diagnosis and treatment. Generalizability is guaranteed by validation on different datasets. Applications for real-time monitoring enable people to capture and examine their speech and handwriting, enabling ongoing health evaluation. In order to handle privacy issues, ethical issues, and regulatory compliance, deep learning is potentially useful to improve accuracy of PD diagnosis as well as provide timely insights for efficient patient treatment. This can be achieved through collaboration with healthcare experts (Saravanan et al. 2022).

Followed by Alzheimer's disease, PD which stands for Parkinson's disease is the second most prevalent highly progressing neurological illness (Loh et al. 2021). Using voice recordings and handwriting, techniques from artificial intelligence are utilized for creating a system to perform diagnosis of this disease. DL stands for Deep learning is an artificial intelligence application that allows computers to learn without explicit programming. Intelligence of humans is imitated with the help of learning processes done from the history of data available. The applications of deep learning includes recognition of text, retrieval of information, analysis of market as well as healthcare and so on.