一项关于临床决定因素的前性研究,使人工智能辅助的诊断能够确定腰椎间盘塌的诊断
Sandeep Pattnaik1, Rajneesh Kumar2, Manu Goyal1
1Department of Physiotherapy, Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India.
概括
这项研究确定了诊断腰椎间椎间盘 (PIVD) 的关键临床因素,使人工智能驱动的诊断工具成为可能. 这种方法减少了对昂贵的MRI扫描的依赖,改善了医疗保健的可访问性.
科学领域:
- 整形外科 整形外科 整形外科
- 神经学 神经学
- 人工智能在医学中的应用
背景情况:
- 腰椎间椎间盘 (PIVD) 是腰部疼痛和残疾的重要原因.
- 磁共振成像 (MRI) 是PIVD诊断的黄金标准,但通常是难以获得和昂贵的.
- 目前用于PIVD的人工智能 (AI) 诊断模型主要依赖于放射学数据,而不是临床参数.
研究的目的:
- 确定诊断腰部PIVD的关键临床决定因素.
- 建立一个开发用于PIVD的AI驱动诊断模型的基础.
- 提供MRI的替代诊断方法,特别是在资源有限的环境中.
主要方法:
- 从2023年10月到2024年1月进行的前性横截面研究.
- 三阶段的方法:文献综述,对PIVD患者的采访 (n=12),以及脊椎专家的专家意见调查 (n=12).
- 使用频率分布和描述性统计数据进行数据分析,以确定临床决定因素.
主要成果:
- 确定和分类腰部PIVD的关键临床决定因素.
- 人口因素:年龄25-50岁.
- 人类测量因素:BMI>25公斤/m2.
- 症状因素:腰部疼痛,辐射疼痛,神经缺陷,异常姿势,腰部运动范围有限.
- 职业因素:长时间坐着 (>6小时),举重.
- 专家验证证实了这些决定因素的相关性.
结论:
- 鉴定到的临床决定因素有助于从依赖于MRI的诊断转向人工智能辅助的腰部PIVD临床评估.
- 将这些临床参数集成到AI算法中可以提高诊断准确性和患者管理.
- 这种方法旨在减少对PIVD诊断的昂贵成像方法的依赖.
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