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Artificial Intelligence in Spasticity Assessment.

Stefano Carda1, Franco Molteni2, Elisa Grana1

  • 1Department of Clinical Neurosciences, Service Universitaire de Neurorehabilitation, Lausanne University Hospital, Av. Pierre-Decker 5, Lausanne CH-1011, Switzerland.

Physical Medicine and Rehabilitation Clinics of North America
|April 30, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances spasticity assessment with accurate sensor systems and computer vision. While promising for objective evaluation, AI integration requires further validation and clinical workflow development.

Keywords:
Artificial intelligenceDigital health technologiesMachine learning algorithmsRehabilitation medicineSpasticity assessment

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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.