Acoustic Method for Detection Large Pneumothoraxes in the Out-of-Hospital Environment

Pneumothoraxes and hemothoraxes present a medical emergency that can easily be life threatening. However, improper and unnecessarily aggressive treatment is not uncommon and poses significant risk to patients. Much of this is a result of poor diagnostic aids to assist in the detection of pneumothoraxes out-of-hospital, such as by Emergency Medical Service (EMS) providers. A simple, portable, and accurate method of clinically detecting a pneumothorax would increase the quality of patient care in austere environments. We propose to evaluate the acoustic transfer function of the human thorax for signs that a pocket of air or blood has accumulated within the pleural space. In an attempt to validate this hypothesis, we constructed a phantom model of the human thorax with artificial lungs and pleural spaces capable of being inflated with variable sized air pockets to displace the lungs. The model was subjected to a known acoustic input in the form of a frequency sweep and a low frequency square wave and the output signal was recorded at multiple points on the phantom. Analysis of four characteristics of the output revealed no discernable correlation to the volume or location of a pneumothorax. While the initial attempt to verify the hypothesis was unsuccessful, the theory remains intact. Future work to further this hypothesis could include improving the mathematical model and/or creating a higher fidelity phantom model for experimentation.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Motz, Andrew J.

Advisor dc:contributor.advisor

• Hanumara, Nevan

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151822

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151822