Mechanical Imaging of Soft Tissues With Miniature Climbing Robots
Mechanical Imaging of Soft Tissues With Miniature Climbing Robots
复制标题
DOI：
10.1109/tbme.2021.3070585
复制
发表时间：
2021-10-01
影响因子：
4.6
通讯作者：
Paradiso, Joseph A.
中科院分区：
文献类型：
--
作者：
Dementyev, Artem;Jitosho, Rianna;Paradiso, Joseph A.
系统地绘制皮肤和组织的力学特性图对于生物力学研究和疾病诊断是有用的。例如，晚期乳腺癌和淋巴瘤表现为皮肤下的硬结节。目前，机械测量是手动进行的，使用触觉或手持工具。手动测量不提供定量信息，并根据从业者的技能而有所不同。研究表明，触觉传感器可能比手更敏感。我们提出了一种方法，使用我们之前开发的皮肤爬行机器人来非侵入性地测试软组织的机械性能。机器人比人类更具系统性和可重复性。使用集成到微型机器人中的切割机或压痕器收集的数据，我们训练卷积神经网络来分类肿块的大小和深度。对于直径为0到10 mm的肿块，该分类对于直径为0到10 mm的肿块的切割仪准确率为98.8%，对于嵌入在模拟组织中的深度为1到5 mm的肿块，分类准确率为99.6%。我们在前臂上进行了有限的评估，机器人用切割器对干燥的皮肤进行了成像。我们希望提高对组织进行非侵入性检测的能力，最终提供更好的灵敏度和系统的数据收集。
Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depending on the skill of the practitioner. Research shows that tactile sensors could be more sensitive than a hand. We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. Robots are more systematic and repeatable than humans. Using the data collected with a cutomoter or indenter integrated into the miniature robot, we trained a convolutional neural network to classify the size and depth of the lumps. The classification works with 98.8% accuracy for cutometer and 99.6% for indenter for lump size with a diameter of 0 to 10 mm embedded in depth of 1 to 5 mm in a simulated tissue. We conducted a limited evaluation on a forearm, where the robot imaged dry skin with a cutometer. We hope to improve the ability to test tissues noninvasively, and ultimately provide better sensitivity and systematic data collection.