Ruiz-Vitte A, Gutiérrez-Fernández M, Laso-García F et al. Comput Biol Med. 2025 Mar;186:109689. doi: 10.1016/j.compbiomed.2025.
https://pubmed.ncbi.nlm.nih.gov/39862465/
Abstract: 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.
Funding: This study was supported by the Instituto de Salud Carlos III (ISCIII) PI20/00243, co-funded by the European Union; RICORS network RD21/ 0006/0012 and the Next Generation EU funding that finances the actions of the Recovery and Resilience Mechanism; Miguel Servet CPII20/ 00002 to MG-F; FI18/00026 to FL-G. and FI17/00188 to MCG-F and by the Spanish Ministry of University, Recovery, Transformation and Resilience Plan and the Universidad Aut´ onoma de Madrid under grant CA1/RSUE/2021-00753 to DP-A.