Artificial Intelligence in the Assessment and Management of Spasticity: A Scoping Review

Abstract

Spasticity is a prevalent and disabling consequence of upper motor neuron disorders, yet its assessment and management remains clinically challenging. Artificial intelligence (AI) may offer opportunities to enhance the objective assessment, prediction, and management of spasticity; however, the current evidence base has not been comprehensively characterised. This review systematically mapped evidence on the application of AI across the continuum of spasticity care and identified key evidence gaps to inform future research. PubMed, Embase, CINAHL, and Web of Science were searched for peer-reviewed English-language studies evaluating AI in adult spasticity. Two reviewers independently screened studies, extracted data, and assessed methodological quality using NHLBI tools and PROBAST + AI. This scoping review was prospectively registered with the Open Science Framework Registries. The search yielded 1832 records, of which 21 studies were included after screening. One additional study was identified through citation searching, bringing the total to 22 studies involving 1114 participants. Applications spanned four clinical domains: discrimination between spastic and healthy individuals (n = 2), severity assessment (n = 18), risk prediction (n = 1), and treatment response prediction (n = 1). Most studies focused on post-stroke upper limb spasticity. Surface electromyography was the most frequently used input modality, often integrated with kinematic or kinetic sensors. Conventional machine learning predominated, with reported accuracies of 74–96% and the Modified Ashworth Scale as the most common reference standard. All 22 studies were at high overall risk of bias on PROBAST + AI, and none performed external validation, highlighting substantial methodological limitations that constrain clinical translation.