The quantitative evaluation of motor function in experimental stroke models is essential for the preclinical assessment of new therapeutic strategies that can be transferred to clinical research
however, conventional assessment tests are hampered by the evaluator's subjectivity. We present an artificial intelligence-based system for the automatic, accurate, and objective analysis of target parameters evaluated by the ledged beam walking test, which offers higher sensitivity than the current methodology based on manual and visual counting. This system employs a residual deep network model, trained with DeepLabCut (DLC) to extract target paretic hindlimb coordinates, which are categorized to provide a ratio measurement of the animal's neurological deficit. The results correlate with the measurements performed by a professional observer and have greater reproducibility, easing the analysis of motor deficits and providing a reliable and useful tool applicable to other diseases causing motor deficits.