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.

中文翻译：

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在355 nm激光斑块切除术过程中使用测得的声信号对组织进行后分类

当前的激光旋磨术技术采用逐步（SBS）方法（无导丝）来治疗具有挑战性（交叉）的慢性完全闭塞患者，缺乏对消融组织的实时信号监测，并且携带血管穿孔的风险。我们使用在SBS方式下进行的355 nm固态Auryon激光设备进行的五次斑块切除术过程中，通过附近放置的麦克风记录的声波信号，对消融的组织进行了首次分类后的分类，其中一些钙化非常严重。使用我们的机器学习算法，来自五位患者的这些消融信号记录的分类结果显示，动脉壁和非动脉壁材料的分类准确性为93.7％。尽管仍处于非常初步的阶段，需要进一步研究，然后再作为商用设备使用，在SBS案例中，这些首次声学后分类的结果非常有前途。总体而言，该研究表明，使用非接触式麦克风在线记录声信号可能会潜在地有助于SBS病例中消融组织的在线分类。该技术可用于确认在脉管系统中的正确定位，并以此潜在地在此类手术中使用355 nm激光斑块切除术进一步降低穿孔的风险。