软机器人在医疗程序中的集成显着改善了诊断和治疗干预措施,解决安全问题并提高外科医生的灵活性。结合人工智能,这些软机器人具有加速自主干预的潜力,如用于癌症检测的组织触诊。虽然摄像机在手术器械中很普遍,视线模糊的情况需要触诊。这项概念验证研究了在模拟体内检查大肠中使用集成了电阻抗断层扫描(EIT)功能的软机器人进行组织触诊的有效性。该方法涉及使用触诊期间在液压驱动的软连续体机器人上引起的形状变化,将不同厚度的组织样本分类为健康组织和癌组织。机器人的形状变化使用EIT映射,提供阻抗测量阵列。在制造平面内弯曲软操纵器之后,初步的组织体模设计是详细的。幻影,代表降结肠壁,基于质量弹簧模型考虑周围组织的诱导刚度。操纵器的形状变化,由于与不同硬度的组织相互作用,被测量,和EIT测量值被馈送到长短期记忆(LSTM)分类器中。训练和测试数据集被收集为来自单个训练体模和两个测试体模的数据的时间序列,即,A和B,具有独特的厚度图案。从幻影B收集的数据集,刚度分布不同,仍然看不到网络,从而对分类器提出了挑战。分类器和所提出的方法在体模A和B上分别达到93%和88.1%的准确率,分别。分类结果通过混淆矩阵和热图呈现,可视化算法的准确性和相应的分类组织。
The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated in vivo inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of 93 % and 88.1 % on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and corresponding classified tissues.