Department of Orthopaedic Surgery, Otto-von-Guericke University/University Hospital, Magdeburg, Germany; SURAG Medical GmbH, Leipzig, Germany.
SURAG Medical GmbH, Leipzig, Germany.
Comput Biol Med. 2023 Sep;164:107272. doi: 10.1016/j.compbiomed.2023.107272. Epub 2023 Jul 19.
The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions.
We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor.
Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues.
Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.
微创手术的发展趋势使得外科医生可获得的触觉信息大大减少，补偿策略从基于视觉的技术到将传感概念集成到手术器械中不等。触觉信息对于触诊任务至关重要，例如组织的区分或表面的特征描述。这项工作研究了一种新的传感方法，从源自器械-组织相互作用的振动信号中得出触诊相关信息。
我们进行了一项可行性研究，以根据表面触诊来区分三种非动物和三种动物组织标本。传感器配置安装在标准器械的近端，与组织相互作用点相对。采集了 1680 次触诊事件的振动声信号，并使用连续小波变换计算时变频谱。为了验证，针对随后使用线性支持向量机和 k-最近邻的分类，计算了 9 个与光谱能量相关的特征。
源自振动信号的指标在属于同一组织标本的一组触诊中非常稳定，与触诊的主体无关。组织标本表面纹理的差异反映在这些指标中，并可作为区分的基础。经过监督学习方法的分类，三种组织分类任务的准确率>93.8%，而六种组织的组合准确率下降到 78.8%。
简单的特征源自振动声信号，有助于区分生物组织，显示出所提出方法提供与相互作用组织相关信息的潜力。结果鼓励进一步研究微创手术中这一尚未充分利用的信息源。
