一个中性学可解释的AI框架,用于模拟沉浸式立体术神经外科模拟中的不确定性
Jesus Rafael Hechavarria-Hernandez1
1Universidad Ecotec, Samborondón, Ecuador.
Frontiers in neurology
|March 11, 2026
概括
一个新的Neutrosophic Explainable AI (N-XAI) 框架通过在虚拟现实 (VR) 中建模性能来增强外科培训. 它准确地识别了传统指标错过的不确定的外科行为,改善了神经外科手术能力评估.
科学领域:
- 医疗教育 技术 技术 医学教育
- 人工智能在医学中的应用
- 手术模拟器手术模拟器
背景情况:
- 虚拟现实 (VR) 和人工智能 (AI) 的整合将彻底改变医学教育.
- 立体神经外科手术 (例如,深度大脑刺激) 目前的性能指标通常是二元的,无法检测到关键的不确定的行为,如不稳定或犹.
- 这种限制可能会导致在高风险的程序中以狭窄的安全边际进行不准确的能力分类.
研究的目的:
- 引入一个新的Neutrosophic可解释AI (N-XAI) 框架用于外科手术绩效评估.
- 使用三个维度来建模外科性能:能力 (真相),不稳定性/模糊性 (不确定性) 和错误 (虚假性).
- 用单值中性质集 (SVNS) 来量化精度稳定空间中的性能.
主要方法:
- 生成一个合成数据集,包含专家,不确定和初学者技能组的60次模拟手术尝试.
- 计算每个模拟尝试的中性学能力分数.
- 使用非参数统计测试进行分组差异化的分数分析.
主要成果:
- N-XAI框架成功地区分了专家,不确定的和新手手术技能组.
- 它确定了高风险,不确定的病例,具有可接受的空间准确性,但显著的不稳定性,这是传统指标忽视的.
- 中性能力评分有效量化了表现细微差别,超出了简单的准确性.
结论:
- 拟议的N-XAI框架为评估沉浸式神经外科模拟中的不确定性提供了一种数学上可靠和可解释的方法.
- 通过明确解决不确定性,它增强了VR培训系统的诊断能力.
- 这种方法为未来在现实世界的立体神经外科模拟中进行验证提供了基础.
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