School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Eximo Medical Ltd., Rehovot, Israel.
J Biophotonics. 2021 Mar;14(3):e202000185. doi: 10.1002/jbio.202000185. Epub 2020 Dec 27.
The current laser atherectomy technologies to treat patients with challenging (to-cross) total chronic occlusions with a step-by-step (SBS) approach (without leading guide wire), are lacking real-time signal monitoring of the ablated tissues, and carry the risk for vessel perforation. We present first time post-classification of ablated tissues using acoustic signals recorded by a microphone placed nearby during five atherectomy procedures using 355 nm solid-state Auryon laser device performed with an SBS approach, some with highly severe calcification. Using our machine-learning algorithm, the classification results of these ablation signals recordings from five patients showed 93.7% classification accuracy with arterial vs nonarterial wall material. While still very preliminary and requiring a larger study and thereafter as commercial device, the results of these first acoustic post-classification in SBS cases are very promising. This study implies, as a general statement, that online recording of the acoustic signals using a noncontact microphone, may potentially serve for an online classification of the ablated tissue in SBS cases. This technology could be used to confirm correct positioning in the vasculature, and by this, to potentially further reduce the risk of perforation using 355 nm laser atherectomy in such procedures.
目前，针对具有挑战性（可穿透）的全慢性闭塞病变患者，采用逐步（SBS）方法（不使用引导导丝）的激光动脉切除术技术缺乏对被切除组织的实时信号监测，并且存在血管穿孔的风险。我们首次在使用 355nm 固态 Auryon 激光设备进行 SBS 方法的五次动脉切除术过程中，使用放置在附近的麦克风记录的声学信号，对切除的组织进行分类，其中一些存在严重钙化。使用我们的机器学习算法，对来自五名患者的这些消融信号记录的分类结果显示，动脉壁与非动脉壁材料的分类准确率为 93.7%。虽然这仍然是初步的结果，需要进行更大规模的研究，并随后作为商业设备，但 SBS 病例中这种声学后分类的初步结果非常有前景。这项研究表明，一般来说，使用非接触式麦克风在线记录声学信号，可能有助于对 SBS 病例中被切除的组织进行在线分类。这项技术可用于确认在血管中的正确定位，从而有可能进一步降低使用 355nm 激光动脉切除术在这些手术中穿孔的风险。
