Artificial Intelligence in Spasticity Assessment

Area of Science:

• Neurology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Spasticity assessment traditionally relies on subjective clinical scales.
• There is a growing need for objective, quantitative, and patient-centered spasticity evaluation methods.
• Technological advancements offer potential solutions for improving spasticity assessment.

Purpose of the Study:

• To review and analyze artificial intelligence (AI) applications in spasticity assessment.
• To examine technological innovations in AI-driven spasticity evaluation from 2020-2024.
• To identify the potential and challenges of AI in spasticity assessment.

Main Methods:

• Literature review of AI applications in spasticity assessment (2020-2024).
• Analysis of sensor-based systems, computer vision, natural language processing, and digital twin technologies.
• Evaluation of accuracy, precision, and capabilities in automated clinical assessment and treatment simulation.

Main Results:

• Sensor-based systems demonstrate 91%-94% accuracy in automated spasticity assessment.
• Computer vision enables precise markerless motion analysis for spasticity.
• Natural language processing facilitates automated extraction of patient goals; digital twins offer personalized treatment simulations.

Conclusions:

• AI represents a paradigm shift towards data-driven, objective, and reliable spasticity evaluation.
• AI technologies promise enhanced patient-centered care in spasticity management.
• Further large-scale validation and seamless clinical integration are crucial for widespread AI adoption in spasticity assessment.