AI-Based Parkinson’s Severity Assessment

Introduction
Parkinson’s disease (PD) is a complex neurodegenerative disorder, characterized by both motor and non-motor symptoms. Traditional methods for assessing PD severity often struggle with accuracy, especially in older patients. These methods typically rely on subjective expert evaluations or complex sensor-based systems, making standardization difficult. Recent advances in AI have opened new possibilities for more accurate, accessible, and standardized PD assessments. This blog post explores a novel AI-based approach using MediaPipe, a contactless pose extraction model, which is integrated with machine learning techniques to enhance PD grading without the use of wearable sensors.

Why Accurate PD Assessment Matters

PD primarily affects the dopaminergic neurons in the brain, leading to a variety of symptoms like resting tremor, bradykinesia, rigidity, and gait impairment. Accurate diagnosis and grading of PD are crucial for effective treatment and management. However, the variability in symptoms presents significant challenges. Traditional diagnostic methods, including speech analysis and sensor-based approaches, have their limitations:

  • Speech Analysis: Techniques analyzing vocal features have shown high accuracy but often face issues with generalizability across different datasets.
  • Sensor-Based Approaches: Although effective, these methods require patients to wear sensors, impacting convenience and compliance.

MediaPipe: A Contactless Solution for PD Assessment

MediaPipe is a powerful AI tool that extracts pose data from video footage. In this PD assessment study, it serves as the foundation for capturing kinematic features related to joint movements.

How MediaPipe Works in PD Assessment

  1. Video-Based Kinematic Feature Extraction:
  • MediaPipe extracts kinematic features from joint movements recorded in videos.
  • It identifies 19 specific kinematic features related to joint angles and lengths, which are essential for evaluating motor impairments in PD.
  • The method is entirely contactless, making it a practical option for home-based assessments.
  1. Geometric Analysis:
  • The extracted features undergo geometric analysis to ensure accurate assessment of joint movements.
  • This step helps in filtering out irrelevant or noisy data, improving the reliability of the overall analysis.
  1. Machine Learning Integration:
  • The processed kinematic data is then fed into five different machine learning algorithms: Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and K-Nearest Neighbors (KNN).
  • Among these models, the KNN classifier demonstrated the highest performance, achieving 96.63% overall accuracy and 100% accuracy in classifying severe PD grades (Grades 4 and 5).

Benefits of This AI-Based Approach

The proposed AI-based PD assessment method offers several advantages:

  1. Standardization: It provides a consistent and objective evaluation, minimizing variability between assessments.
  2. Accessibility: Patients can be assessed remotely without the need for hospital visits or wearable sensors.
  3. Convenience: The contactless nature of this method improves patient compliance and makes home-based monitoring feasible.
  4. Enhanced Accuracy: By integrating AI with kinematic data analysis, this approach yields superior results in detecting and grading PD severity.

Step-by-Step Implementation of the AI-Based Model

  1. Data Collection:
  • Collect video recordings from volunteers across different PD severity grades, following the Hoehn and Yahr (HY) scale criteria.
  1. Feature Extraction:
  • Use MediaPipe to extract pose data, focusing on joint movements.
  1. Geometric Analysis:
  • Conduct geometric operations to analyze joint angles and lengths, filtering out irrelevant features.
  1. Data Integration and Classification:
  • Integrate the processed data into machine learning models and compare their performance.
  1. Model Evaluation:
  • Evaluate the models based on accuracy and reliability in classifying PD grades.

Research Outcomes and Implications

The study successfully demonstrated that an AI-based system utilizing MediaPipe could achieve high accuracy in PD severity assessment. This innovation not only simplifies PD diagnosis but also offers a standardized and sensor-free solution, making it highly valuable for both clinical and remote PD management. Further research could focus on refining this model for broader clinical applications, including real-time monitoring.

Conclusion

This AI-based approach to PD assessment represents a significant advancement in Parkinson’s management. By leveraging MediaPipe for contactless pose extraction and integrating machine learning models, it provides a more accurate, standardized, and accessible way to evaluate PD severity. This solution can help reduce hospital visits, improve patient compliance, and make assessments more feasible for remote settings.

Source

DALL-E Prompt: A detailed watercolor painting of a Parkinson’s patient participating in a video-based assessment at home, showing joint movements being analyzed by an AI interface on a computer screen. The room has warm lighting, a comfortable chair, and soft textures, emphasizing a sense of ease and accessibility.

SEO Keywords: Parkinson’s disease, AI assessment, MediaPipe, PD severity grading, contactless diagnosis

Disclaimer: AI-generated medical content is not a substitute for professional medical advice or diagnosis; I hope you found this blog post informative and interesting. www.parkiesunite.com by Parkie

👋

Sign up to receive notifications of new posts.

We don’t spam!