Reference #: 01526
The University of South Carolina is offering licensing opportunities for Transforming Cheap Spirometers to Estimate Flow-Volume Graph By Deep Learning
Background:
Respiratory diseases, like Asthma, COPD, have been a significant public health challenge over decades, and the recent COVID-19 pandemic has worsened the situation. Portable, home-use spirometers are effective in continuous monitoring of respiratory syndromes out-of-clinic.
Invention Description:
The technology designs a deep residual decoder network for curve learning then takes key indicators as the input and generates a complete flow-volume graph as the output.
Potential Applications:
We believe the invention can be a solution to extend the capability of at home spirometers for finer-grained, long-term lung function monitoring in the post-COVID era.
Advantages and Benefits:
Existing systems are either costly or provide limited spirometry information. This invention leverages all key indicators to identify various lung conditions as normal, obstructive, or restrictive, and other respiratory syndromes.