Center for Modeling, Simulation, and Imaging for Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA.
Boston University Neurophotonics Center, Boston, Massachusetts, 02215, USA.
Sci Data. 2023 Oct 14;10(1):699. doi: 10.1038/s41597-023-02603-3.
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments.
功能性近红外光谱（fNIRS）是一种用于研究移动对象大脑活动的神经影像学工具。开放获取的 fNIRS 数据集仅限于简单和/或受限制运动的任务。在这里，我们报告了一个在实验室环境中对移动对象进行腹腔镜手术基础（FLS）任务的 fNIRS 数据集。在美国，通过腹腔镜手术基础（FLS）任务证明胜任能力是普通外科委员会认证的先决条件。ASTaUND 数据集是通过四个不同的研究获得的。我们提供了有关硬件、FLS 任务执行协议和对象人口统计信息的相关信息，以方便使用这个开放获取的数据集。我们还提供了并发 FLS 评分，这是由 FLS 委员会开发的用于评估手术技能的定量指标。这个数据集有望支持通过神经影像学数据评估手术技能的不断发展的领域，并为在现实、非限制性环境中使用的数据处理管道提供一个示例。
