University of South Carolina School of Medicine, Greenville, SC, 29605, USA.
Clemson University School of Health Research, CUSHR, Clemson, SC, 29634, USA.
Sci Data. 2024 Aug 2;11(1):839. doi: 10.1038/s41597-024-03667-5.
Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identification, brain health quantification, and prognosis. These algorithms thrive on large amounts of information, but require diverse datasets to avoid overfitting to specific populations or acquisitions. While there are many large public MRI datasets, few of these include acute stroke. We describe clinical MRI using diffusion-weighted, fluid-attenuated and T1-weighted modalities for 1715 individuals admitted in the upstate of South Carolina, of whom 1461 have acute ischemic stroke. Demographic and impairment data are provided for 1106 of the stroke survivors from this cohort. Our validation demonstrates that machine learning can leverage the imaging data to predict stroke severity as measured by the NIH Stroke Scale/Score (NIHSS). We share not only the raw data, but also the scripts for replicating our findings. These tools can aid in education, and provide a benchmark for validating improved methods.
中风是导致残疾的主要原因，磁共振成像（MRI）通常用于急性中风的管理。公开分享这些数据集可以帮助开发机器学习算法，特别是用于病灶识别、大脑健康量化和预后。这些算法依赖于大量的信息，但需要多样化的数据集来避免过度拟合特定的人群或采集。虽然有许多大型公共 MRI 数据集，但很少有数据集包含急性中风。我们描述了使用弥散加权、液体衰减和 T1 加权模态对南卡罗来纳州北部 1715 名入院患者的临床 MRI，其中 1461 名患者患有急性缺血性中风。从该队列中，我们为 1106 名中风幸存者提供了人口统计学和损伤数据。我们的验证表明，机器学习可以利用影像学数据来预测中风严重程度，如 NIH 中风量表/评分（NIHSS）所测量的。我们不仅共享原始数据，还共享复制我们发现的脚本。这些工具可以帮助教育，并为验证改进的方法提供基准。
