Development and Validation of a Machine Learning-Derived Kinematic Deviation Index for the Evaluation of Forelimb in Rodents

The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.

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Publication Details

Journal
AI in neuroscience.
Published
2025-09-01
DOI
https://doi.org/10.1177/2997979x251370362
Primary Topic
Spinal Cord Injury Research
Type
article
Field-Weighted Citation Impact
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article

Development and Validation of a Machine Learning-Derived Kinematic Deviation Index for the Evaluation of Forelimb in Rodents

AI in neuroscience.
Spinal Cord Injury Research
article

Development and Validation of a Machine Learning-Derived Kinematic Deviation Index for the Evaluation of Forelimb in Rodents

article en

Abstract

The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.

AI in neuroscience.Vol. 1(3)
Scripps Research Institute (US), University of Waterloo (CA), University of Alberta (CA), University of California, San Francisco (US), Cornell University (US), MIND Research Institute (US), Burke Medical Research Institute (US), Neurological Surgery (US), Weill Cornell Medicine (US), Universidad Nacional Autónoma de México (MX)
Peace, Justice and strong institutions
Openalex Percentile: Top 33%
Spinal Cord Injury Research
DOIOpen Access PDFOpenAlex
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Development and Validation of a Machine Learning-Derived Kinematic Deviation Index for the Evaluation of Forelimb in Rodents · AI in neuroscience. (2025) | TGRS Research Map | TGRS