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Updated: May 24, 2025

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使用视频头脉冲测试将中风与前庭神经炎分离:机器学习模型与专家临床医生对比

Chao Wang^1,2, Jeevan Sreerama¹, Benjamin Nham³

¹Central Clinical School, University of Sydney, Sydney, NSW, Australia.

Journal of neurology

|March 5, 2025

概括

机器学习模型使用视频头部脉冲测试 (VHIT) 数据准确地区分后部循环中风 (PCS) 和前庭神经炎 (VN). 这些人工智能工具在协助急诊室临床医生诊断急性前庭综合征方面表现有前途.

科学领域:

神经学神经学
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

急性前庭神经综合征带来了诊断挑战,区分良性前庭神经炎 (VN) 和危及生命的后部循环中风 (PCS).
视频头脉冲测试 (VHIT) 量化了前庭眼反射,有助于诊断,但需要专家解释.
开发针对急性前体综合征的客观诊断工具对于及时和适当的患者管理至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,仅使用VHIT数据来区分PCS和VN.
与专家临床医生和标准VHIT指标相比,评估ML模型的诊断准确性.

主要方法:

在VHIT的未经编辑的头部和眼睛速度数据上训练ML分类模型,用于急性前体综合征患者.
用第二个机构的独立测试数据集验证的模型.
将ML模型性能与专家临床医生诊断和VHIT增益截止值进行比较.

主要成果:

最好的ML模型在测试数据集 (n=49) 中识别VN时达到87.8%的准确性.
ML模型的性能与失明的专家临床医生 (85.7%的准确率) 相当,并且优于VHIT增益截止值 (75.5%的准确率).
与专家临床医生相比,模型性能差异在统计学上并不显著 (p=0.56),但与增益截止值相比显著 (p=0.01).

结论:

机器学习模型使用高精度的VHIT数据有效地区分PCS和VN.
这些人工智能工具提供了一种有希望的客观方法,以支持急诊室临床医生诊断急性前庭综合征.
基于ML的VHIT分析可以提高诊断能力,通过区分PCS和VN等关键条件来提高患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.一次性中风,中风.静止管神经炎是一种神经炎.视频头部冲动测试测试视频头部冲动测试

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