Ruiz-Vitte Ainhoa, Gutiérrez-Fernández María, Laso-García Fernando, Piniella Dolores, Gómez-de Frutos Mari Carmen, Díez-Tejedor Exuperio, Gutiérrez Álvaro, Alonso de Leciñana María
Neurological Sciences and Cerebrovascular Research Laboratory, Department of Neurology and Stroke Centre, Neurology and Cerebrovascular Disease Group, Neuroscience Area La Paz Institute for Health Research (idiPAZ), (La Paz University Hospital- Universidad Autónoma de Madrid), Spain; ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.
Neurological Sciences and Cerebrovascular Research Laboratory, Department of Neurology and Stroke Centre, Neurology and Cerebrovascular Disease Group, Neuroscience Area La Paz Institute for Health Research (idiPAZ), (La Paz University Hospital- Universidad Autónoma de Madrid), Spain.
Comput Biol Med. 2025 Mar;186:109689. doi: 10.1016/j.compbiomed.2025.109689. Epub 2025 Jan 24.
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.
在实验性中风模型中，运动功能的定量评估对于新治疗策略的临床前评估至关重要，这些新治疗策略可转化为临床研究；然而，传统的评估测试受到评估者主观性的阻碍。我们提出了一种基于人工智能的系统，用于对通过有边缘梁行走测试评估的目标参数进行自动、准确和客观的分析，该系统比当前基于手动和视觉计数的方法具有更高的灵敏度。该系统采用残差深度网络模型，通过DeepLabCut（DLC）进行训练，以提取目标患侧后肢坐标，对其进行分类以提供动物神经功能缺损的比率测量。结果与专业观察者进行的测量相关，并且具有更高的可重复性，简化了运动功能缺损的分析，并提供了一种适用于其他导致运动功能缺损疾病的可靠且有用的工具。
