Sound-guided assessment and localization of pulmonary air leak

Meghan R. Pinezich, Seyed Mohammad Mir, Jonathan A. Reimer, Sarah R. Kaslow, Jiawen Chen, Brandon A. Guenthart, Matthew Bacchetta, John D. O'Neill, Gordana Vunjak-Novakovic, Jinho Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post-surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound-guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free-flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound-guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video-assisted thoracoscopic surgery (VATS) or thoracotomy procedures.

Original languageEnglish
Article numbere10322
JournalBioengineering and Translational Medicine
Volume8
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • Lung disease
  • digital medicine
  • lung volume reduction surgery
  • minimally-invasive diagnosis
  • sound analysis

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