Targeted transcranial direct current stimulation combined with bimanual training for children with cerebral palsy

靶向经颅直流电刺激联合双手训练治疗脑瘫患儿

• 批准号: 10594264
• 负责人: Kathleen Margaret Friel
• 金额: $ 35.64万
• 依托单位: WINIFRED MASTERSON BURKE MED RES INST
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-26 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594264
• 关键词: 3-Dimensional; Address; Adult; Architecture; Cerebral Palsy; Child; Clinical Research; Clinical Trials; Clinical assessments; Data; Data Set; Databases; Development; Enrollment; Ethics; Future; Goals; Gold; Hand; Hand functions; Image; Intervention; Machine Learning; Measures; Methods; Modeling; Motion; Movement; Movement Disorders; Participant; Pattern; Performance; Population; Publishing; Research; Research Personnel; Software Framework; Spastic Cerebral Palsy; System; Testing; Training; Underrepresented Populations; United States National Institutes of Health; Upper Extremity; Upper limb movement; base; cost; cost effective; data acquisition; data repository; deep learning; deep learning model; improved; kinematics; parent grant; repository; time use; tool; transfer learning

PROJECT SUMMARY The overall objective for the parent grant is to determine how to optimally target transcranial direct current stimulation (tDCS) to enhance the efficacy of upper extremity (UE) training in children with unilateral cerebral palsy (UCP). A key tool to determine whether this intervention leads to improves UE function is the use of clinical assessments. However, even the best assessments do not have the capacity to identify kinematic features of a child’s hand movement. Most assessments use time to complete a task as a corollary of hand function or subjective ratings of movement quality and function. By not capturing movement patterns using kinematics, there is a vital loss of information that could help optimize interventions. Existing methods of UE kinematics acquisition are not easily accessible because of their size, cost, and operational complexities. In this supplement proposal, we aim to use Deep Learning (DL) pose estimation models along with 3D depth sensing cameras to develop a cost effective, easy to use, and compact Deep Learning based markerless kinematic data acquisition (DL-KDA) system that can be applied to children with UCP. In order to achieve this overall goal, we must establish the accuracy and validity of the kinematic data obtained from the system. We will begin by developing a modular software framework for building and testing DL-KDA systems against a very precise marker-based motion capture gold standard (VICON). We will study the effects of 3D camera and DL parameters/architecture on the accuracy of the resulting kinematic data from healthy adult participants during BBT. Kinematic data from both, the DL-KDA and gold standard systems along with the respective DL-KDA parameter data will be transformed into an AI/ML ready HDF5 file. This will be published in public DL and NIH data repositories to encourage further development of ML applications using kinematic data. Once a validated and optimal DL-KDA configuration is identified, we will investigate this system’s suitability for applications to children with UCP. Since existing training datasets used for most DL pose estimation models are not inclusive of children and/or adults with movement disorders, potential ethical and scientific biases may arise if applied to an underrepresented group. To address this, we will use our large database of UCP assessment videos over the last 9 years to generate ~12,000 pose images of children with UCP. The Images will be transformed into ML/AI ready HDF5 datasets and published in public DL and NIH repositories. These datasets will be available for other researchers to consider when using or building DL pose estimation models for applications in UCP clinical research. We will use this dataset to perform transfer learning and retrain the optimal DL model previously identified. The performance of the retrained DL model will be statistically compared to the original DL model to verify if bias was indeed present. Finally, we will apply the optimal DL- KDA using the retrained model to ~20 children with UCP during the BBT. Validated kinematics for the UCP population, as well, will be uploaded to public DL and NIH repositories for use in future UCP research.

项目摘要 父授予的总体目标是确定如何最佳靶向thrancranial直流电流 刺激（TDC）以提高单侧大脑儿童上肢训练的效率（UE） 麻痹（UCP）。确定这种干预是否导致UE功能的关键工具是使用 临床评估。但是，即使是最佳评估也没有能力识别运动学 孩子手动的特征。大多数评估都用时间来完成任务作为手的推论 运动质量和功能的功能或主观评分。通过不使用使用运动模式 运动学，信息的重要损失可以帮助优化干预措施。 UE的现有方法 运动学的尺寸，成本和操作复杂性不容易访问。 在此补充建议中，我们旨在使用深度学习（DL）姿势估计模型以及3D 深度感测相机以开发成本效益，易于使用和紧凑的深度学习 可以应用于UCP儿童的无标记运动数据采集（DL-KDA）系统。为了 实现这一总体目标，我们必须确定从 系统。我们将首先开发一个模块化软件框架，用于构建和测试DL-KDA系统 针对非常精确的基于标记的运动捕获金标准（VICON）。我们将研究3D的影响 相机和DL参数/体系结构有关健康成人产生的运动学数据的准确性 BBT期间的参与者。来自DL-KDA和黄金标准系统的运动学数据以及 DL-KDA参数数据将转换为AI/ML Ready HDF5文件。这将在 公共DL和NIH数据存储库，以鼓励使用运动学数据进一步开发ML应用程序。 一旦确定了经过验证且最佳的DL-KDA配置，我们将研究该系统的适用性 向UCP儿童申请。由于用于大多数DL姿势估计模型的现有培训数据集 不包括运动障碍的儿童和/或成年人，潜在的道德和科学偏见可能 如果适用于代表性不足的群体，则会出现。为了解决这个问题，我们将使用UCP的大数据库 在过去的9年中，评估视频产生了约12,000张UCP儿童的姿势图像。图像 将转换为ML/AI Ready HDF5数据集，并在公共DL和NIH存储库中发布。这些 数据集将用于其他研究人员使用或构建DL姿势估计模型时考虑 用于UCP临床研究中的应用。我们将使用此数据集执行转移学习并重新训练 先前确定的最佳DL模型。再培训DL模型的性能将在统计上是 与原始DL模型相比，以验证是否确实存在偏差。最后，我们将应用最佳DL- 在BBT期间，KDA使用重新训练模型对约有20名UCP儿童。验证了UCP的运动学 人口也将被上传到公共DL和NIH存储库，以在未来的UCP研究中使用。