مشخصات پژوهش

The necessity of studying deep learning for diagnosing Parkinson's disease (PD) is underscored by several key factors that highlight its potential to enhance early detection and improve patient outcomes. Importance of Early Diagnosis. 1. Early Intervention: Early diagnosis of PD is crucial as it allows for timely interventions that can slow disease progression and manage symptoms more effectively. Traditional diagnostic methods often rely on clinical assessments, which may not detect PD until significant motor symptoms appear. Deep learning models can analyze subtle patterns in data that are indicative of PD, potentially identifying the disease in its early stages before clinical symptoms manifest [10,11]. .2. Enhanced Accuracy and Reliability: Deep learning algorithms have shown promising results in improving the accuracy of PD diagnoses compared to conventional methods. By utilizing large datasets from various sources, including voice recordings, handwriting samples, and neuroimaging data, these models can learn complex features that are often missed by human evaluators. This capability is particularly beneficial for distinguishing PD from other similar neurological disorders [10,12]. 3. Integration with Wearable Technology: The rise of wearable devices has facilitated continuous monitoring of patients' motor functions and other relevant health metrics. Deep learning can process this data in real-time, enabling proactive management of the disease and personalized treatment plans tailored to individual patient needs [11,13]. 4. Diverse Data Utilization: Recent studies have explored the use of various data types, such as voice patterns and handwriting dynamics, in conjunction with deep learning techniques. This multifaceted approach allows for a more comprehensive understanding of PD and enhances the robustness of diagnostic models [10,14,15].