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<p>Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by motor and non-motor symptoms that complicate early diagnosis. Traditional diagnostic methods often rely on subjective assessments, leading to delays in detection. This paper reviews recent advancements in deep learning applications for PD diagnosis across various modalities, including speech analysis, wearable sensor data, handwriting patterns, medical imaging, EEG signals, and voice recordings. Innovative models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning techniques have demonstrated high accuracy in identifying PD-related patterns. Studies highlight the potential of low-frequency speech features, inertial sensor data, and handwriting analysis in early detection. Advanced imaging models like DAT-Net and PARNet enhance differential diagnosis using PET and SPECT scans. Non-invasive methods analyzing saliva metabolites and EEG signals offer promising avenues for accessible screening. Despite these advancements, challenges remain, including the need for large, high-quality datasets and improved model interpretability for clinical adoption. Addressing these limitations through interdisciplinary collaboration and explainable AI techniques is crucial. In conclusion, deep learning presents significant potential to transform PD diagnosis, enabling earlier detection and personalized treatment strategies, ultimately improving patient outcomes and quality of life.</p>

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A Comparative Analysis of Various Deep Learning-Based Algorithms for the Diagnosis and Detection of Parkinson’s Disease on Different Datasets

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Shreya Mishra,
  • Pancham Mehta,
  • Komal Naaz,
  • Ravishankar Mehta

摘要

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by motor and non-motor symptoms that complicate early diagnosis. Traditional diagnostic methods often rely on subjective assessments, leading to delays in detection. This paper reviews recent advancements in deep learning applications for PD diagnosis across various modalities, including speech analysis, wearable sensor data, handwriting patterns, medical imaging, EEG signals, and voice recordings. Innovative models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning techniques have demonstrated high accuracy in identifying PD-related patterns. Studies highlight the potential of low-frequency speech features, inertial sensor data, and handwriting analysis in early detection. Advanced imaging models like DAT-Net and PARNet enhance differential diagnosis using PET and SPECT scans. Non-invasive methods analyzing saliva metabolites and EEG signals offer promising avenues for accessible screening. Despite these advancements, challenges remain, including the need for large, high-quality datasets and improved model interpretability for clinical adoption. Addressing these limitations through interdisciplinary collaboration and explainable AI techniques is crucial. In conclusion, deep learning presents significant potential to transform PD diagnosis, enabling earlier detection and personalized treatment strategies, ultimately improving patient outcomes and quality of life.