人工智能诊断模型辅助癫痫脑电图诊断专家共识

doi:10.3969/j.issn.1004-583X.2026.02.011

摘要/Abstract

摘要：

本共识系统阐述了人工智能(Artificial Intelligence,AI)诊断模型在辅助癫痫脑电图分析中的应用框架与实践路径。针对目前脑电图人工判读存在的主观性强、效率低等局限,共识明确了AI作为“医师助手”的辅助定位,从数据采集标注、算法开发验证、临床整合应用及安全伦理监管等方面提出全流程标准化建议。旨在推动AI工具科学、安全、有效地融入癫痫诊疗流程,提升诊断效率与一致性,为相关研发、应用与监管工作提供参考依据。

关键词: 癫痫, 脑电图, 人工智能, 辅助诊断, 专家共识

Abstract:

This expert consensus establishes a comprehensive framework and practical roadmap for applying artificial intelligence (AI) diagnostic models to support electroencephalogram (EEG) analysis in epilepsy. In light of the current drawbacks of manual EEG interpretation, primarily subjectivity and limited efficiency, the consensus frames AI as a clinical decision-support tool that augments, rather than replaces, physician expertise. It issues standardized, full-lifecycle recommendations spanning data acquisition and annotation, algorithm development and validation, clinical integration and workflow implementation, and governance covering safety, ethical considerations, and regulatory oversight. The objective is to enable the scientific, safe, and effective incorporation of AI into epilepsy care, thereby enhancing diagnostic accuracy, efficiency, and inter-rater consistency, and to provide practical guidance for researchers, clinicians, developers, and regulators.

Key words: epilepsy, electroencephalogram, artificial intelligence, assisted diagnosis, expert consensus

中图分类号:

R742.1

河北医科大学第二医院神经外科癫痫亚专业组人工智能诊断模型辅助癫痫脑电图诊断专家共识工作组. 人工智能诊断模型辅助癫痫脑电图诊断专家共识[J]. 临床荟萃, 2026, 41(2): 164-168.

Task Force on the Expert Consensus for Artificial Intelligence Diagnostic Model-Assisted Electroencephalography Diagnosis of Epilepsy in the Epilepsy Subspecialty Group of the Department of Neurosurgery of the Second Hospital of Hebei Medical University. Expert consensus on deploying AI diagnostic models to support electroencephalogram-based epilepsy diagnosis[J]. Clinical Focus, 2026, 41(2): 164-168.

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链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2026.02.011

https://www.lchc.cn/CN/Y2026/V41/I2/164

图/表 2

参考文献 13

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核心功能	核心算法	预期用途
伪差去除功能	基于信号处理技术的算法	辅助医生阅读脑电图,排除干扰信号
通过深度学习模型获取包含生理波形片段	基于注意力机制的深度学习模型	区分只包含生理波形的脑电和出现异常放电的脑电
基于特征的异常放电检测算法	机器学习分类器	辅助医生找到脑电图中癫痫样放电的位置
判断脑电是否有癫痫样放电	基于自适应阈值的算法	辅助医生给出脑电图关于癫痫样放电的结论
癫痫样放电的导联定位	基于自适应阈值的算法	辅助医生将癫痫样放电定位在具体导联上

核心功能	核心算法	预期用途
伪差去除功能	基于信号处理技术的算法	辅助医生阅读脑电图,排除干扰信号
通过深度学习模型获取包含生理波形片段	基于注意力机制的深度学习模型	区分只包含生理波形的脑电和出现异常放电的脑电
基于特征的异常放电检测算法	机器学习分类器	辅助医生找到脑电图中癫痫样放电的位置
判断脑电是否有癫痫样放电	基于自适应阈值的算法	辅助医生给出脑电图关于癫痫样放电的结论
癫痫样放电的导联定位	基于自适应阈值的算法	辅助医生将癫痫样放电定位在具体导联上

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