使用多层感知子分类器在弹性组织幻影中振动声学检测内含物:一项概念验证研究
Mostafa Sayahkarajy1, Florian Römer2, Hartmut Witte1
1Group of Biomechatronics, Fachgebiet Biomechatronik, Technische Universität Ilmenau, Ilmenau, D-98693, Germany.
Journal of the mechanical behavior of biomedical materials
|November 22, 2025
概括
这项研究引入了一种新的方法,使用振动声学信号和神经网络来检测组织幻影中模拟瘤的硬性入. 这种方法获得了75%的F1得分,证明了微创手术瘤定位的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 手术技术 手术技术
- 机器学习在医学中的应用
背景情况:
- 准确的瘤边界检测对于瘤手术的成功至关重要.
- 传统的触摸在最小侵入性手术 (MIS) 中是不可行的.
- 需要使用非侵入性方法来评估MIS中的组织机械性质.
研究的目的:
- 为了研究使用振动声学特征检测更硬的内含物 (模拟瘤) 的可行性.
- 开发一种实时检测方法,用于模拟组织幽灵中的瘤.
- 为了评估基于波纹的多层感知神经网络的纳入分类.
主要方法:
- 使用静态测量探头和微电机系统 (MEMS) 传感器.
- 从模拟的组织幽灵获得短时间 (1秒) 的振动声学信号,具有更硬的内含.
- 采用监督学习方法,使用基于波纹的多层感知子 (MLP) 进行二进制分类.
主要成果:
- 在MLP分类器中,MLP在包含检测方面获得了75%的F1分数.
- 该模型在以前未见过的测试数据上显示了77.8%的准确性.
- 性能与支持矢量机 (SVM) 分类器相当.
结论:
- 这项研究支持这样的假设,即改变的振动声学特征可以表明更硬的入.
- 开发的方法显示了作为MIS瘤检测的概念验证的希望.
- 未来的工作包括对生物组织的系统增强和验证.
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