Analyzing drilling noise in rotational atherectomy: Improving safety and effectiveness through visualization and anomaly detection using autoencoder—A preclinical study
Open Access
- 28 November 2023
- journal article
- research article
- Published by Wiley in Health Science Reports
- Vol. 6 (12) , e1739
Abstract
Background and Aims: As the population of aging societies continues to grow, the prevalence of complex coronary artery diseases, including calcification, is expected to increase. Rotational atherectomy (RA) is an essential technique for treating calcified lesions. This study aimed to assess the usefulness of the drilling noise produced during rotablation as a parameter for evaluating the safety and effectiveness of the procedure.Methods: A human body model mimicking calcified stenotic coronary lesions was constructed using plastic resin, and burrs of sizes 1.25 and 1.5 mm were utilized. To identify the noise source during rotablation, we activated the ROTAPRO™ rotablator at a rotational speed of 180,000 rpm, recording the noise near the burr (inside the mock model) and advancer (outside). In addition to regular operation, we simulated two major complications: burr entrapment and guidewire transection. The drilling noise recorded in Waveform Audio File Format files was converted into spectrograms for analysis and an autoencoder analyzed the image data for anomalies.Results: The drilling noise from both inside and outside the mock model was predominantly within the 3000 Hz frequency domain. During standard operation, intermittent noise within this range was observed. However, during simulated complications, there were noticeable changes: a drop to 2000 Hz during burr entrapment and a distinct squealing noise during guidewire transection. The autoencoder effectively reduced the spectrogram data into a two‐dimensional representation suitable for anomaly detection in potential clinical applications.Conclusion: By analyzing drilling noise, the evaluation of procedural safety and efficacy during RA can be enhanced.Keywords
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