多模式和多视图宫脊椎病成像数据集
Qi-Shuai Yu1,2, Jing-Yang Shan3,4, Jie Ma1
1Department of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
Scientific data
|July 2, 2025
概括
这项研究介绍了MMCSD,这是一个用于宫脊椎病的新型多式成像数据集. 它被用来开发一个深度学习模型,预测术后部疼痛,帮助临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 整形外科 整形外科 整形外科
背景情况:
- 宫脊椎病的诊断和评估在很大程度上依赖于多模式和多视图成像.
- 深度学习 (DL) 显示出改善椎椎病的临床管理和研究的前景.
- 公共可用的数据集对于开发和验证DL模型至关重要.
研究的目的:
- 介绍多模态宫脊椎病数据集 (MMCSD),这是宫脊椎病研究的新资源.
- 开发和验证使用MMCSD预测术后部疼痛的DL模型.
- 为了证明MMCSD在推进DL在宫脊椎病和部疼痛的应用中的实用性.
主要方法:
- 汇编了250名宫脊髓炎患者的多模式和多视图成像数据集 (MMCSD).
- 包含各种成像模式:MRI (斜体T1/T2,轴心T2) 和CT (轴心骨/软组织窗口).
- 开发一个DL模型,利用MMCSD来预测术后部疼痛.
主要成果:
- MMCSD数据集成功创建并公开共享.
- 使用MMCSD开发了一个DL模型,证明了它在预测术后部疼痛方面的能力.
- 该研究验证了MMCSD在DL模型开发和临床应用中的可用性.
结论:
- 该MMCSD是推进DL研究在宫脊椎病的宝贵资源.
- 开发的DL模型显示了改善术后部疼痛预测的潜力.
- 这项工作有助于更好的临床诊断评估和宫脊椎病和相关的部疼痛的治疗决策.
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