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Tissue post-classification using the measured acoustic signals during 355 nm laser atherectomy procedures
Ziv Alperovich1, Oshrat Cohen2, Yossi Muncher2
1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Journal of Biophotonics
|November 17, 2020
Summary
This study introduces acoustic signal analysis for real-time tissue classification during laser atherectomy (LA) procedures. This novel method shows high accuracy in distinguishing arterial from non-arterial tissue, potentially reducing perforation risks.
Area of Science:
- Cardiovascular Interventions
- Biomedical Engineering
- Machine Learning in Medicine
Background:
- Current laser atherectomy (LA) technologies for challenging total chronic occlusions lack real-time feedback on ablated tissue.
- Existing LA methods, particularly the step-by-step (SBS) approach without a leading guidewire, carry a risk of vessel perforation.
- There is a need for improved intra-procedural monitoring to enhance safety and efficacy in LA.
Purpose of the Study:
- To investigate the feasibility of using acoustic signals for post-classification of ablated tissues during SBS laser atherectomy.
- To evaluate the accuracy of a machine-learning algorithm in differentiating arterial from non-arterial tissue based on acoustic data.
- To explore the potential of acoustic monitoring to reduce vessel perforation risk in LA procedures.
Main Methods:
- Acoustic signals were recorded using a noncontact microphone during five laser atherectomy procedures employing a 355 nm solid-state Auryon laser device.
- Procedures utilized a step-by-step (SBS) approach, with some cases involving highly calcified occlusions.
- A machine-learning algorithm was developed and applied to classify the recorded acoustic signals.
Main Results:
- The machine-learning algorithm achieved 93.7% classification accuracy in distinguishing between arterial and non-arterial wall material.
- These results represent the first reported acoustic post-classification of ablated tissues in SBS laser atherectomy cases.
- The preliminary findings indicate promising potential for acoustic signal analysis in this context.
Conclusions:
- Online acoustic signal recording shows potential for real-time tissue classification during SBS laser atherectomy.
- This technology could confirm correct positioning within the vasculature, thereby potentially reducing the risk of vessel perforation.
- Further studies with larger cohorts and commercial development are warranted to validate and implement this promising technique.
