Pneumonia is one of the leading causes of death in the world and affects more than one million people each year in the United States. The disease disproportionately affects children, the elderly, and hospitalized patients. To give them the greatest chance of recovery, it is important to detect and treat them early. Current diagnostic methods include a variety of blood tests and chest computed tomography, and doctors need to suspect pneumonia before ordering.
Jin Yong Jeon of Hanyang University will discuss a diagnostic technique pneumonia through passive listening during his session, “The algorithm for diagnosis of pneumonia is based on the impulse response in the room using cough sounds.” The presentation will take place December 5 at 4:20 p.m. Eastern at Summit C, as part of the 183rd Meeting of the Acoustical Society of America running from May 5 to December 9 at the Grand Hyatt Nashville.
Jeon and fellow researchers developed a machine learning algorithm to identify cough sounds and determine if a subject has pneumonia. Because every room and recording device is different, they enhanced their recording with room impulse responses, measuring how well the acoustics of a space respond to different frequencies of sound. By combining this data with recorded cough sounds, the algorithm can work in any environment.
“Automatically diagnose a health status Through information about cough sounds that occur continuously in daily life will facilitate non-direct treatment,” said Mr. Jeon. “It can also reduce overall medical costs.”
Now, one company has a plan to apply this algorithm for remote patient monitoring. The team is also looking to implement it as a home care app, and they plan to make the experience simpler and more user-friendly.
“Our research team is planning to automate each step of the process that is currently being done manually to improve convenience and applicability,” said Mr. Jeon.
Acoustical Society of America
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