18275170. Methods for Pulmonary Function Testing With Machine Learning Analysis and Systems for Same simplified abstract (THE REGENTS OF THE UNIVERSITY OF CALIFORNIA)

Methods for Pulmonary Function Testing With Machine Learning Analysis and Systems for Same

Organization Name

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor(s)

Franklin Heng of San Francisco CA (US)

Xavier Minh Mathieu Orain of San Francisco CA (US)

Methods for Pulmonary Function Testing With Machine Learning Analysis and Systems for Same - A simplified explanation of the abstract

This abstract first appeared for US patent application 18275170 titled 'Methods for Pulmonary Function Testing With Machine Learning Analysis and Systems for Same

Simplified Explanation

The present invention provides methods and systems for pulmonary function testing of a subject, including generating flow volume curves, computing lung function parameters, and determining potential clinical interpretations of pulmonary function.

• Measures lung function without initial calibration of spirometer information
• Uses spirometer information to develop a machine learning algorithm for measuring lung function without needing spirometer information
• Computes metrics such as chest and waist width and sitting height of subject

Potential Applications

This technology could be applied in medical settings for diagnosing and monitoring respiratory conditions, as well as in research settings for studying lung function in various populations.

Problems Solved

This technology addresses the need for accurate and efficient pulmonary function testing, without the requirement for initial calibration of spirometer information. It also offers the potential for developing machine learning algorithms to improve lung function measurement.

Benefits

The benefits of this technology include improved accuracy in measuring lung function, the ability to compute additional metrics related to the subject's physical characteristics, and the potential for automation through machine learning algorithms.

Potential Commercial Applications

Potential commercial applications of this technology could include medical devices for pulmonary function testing, software for analyzing lung function data, and research tools for studying respiratory health.

Possible Prior Art

Prior art in the field of pulmonary function testing includes traditional spirometry methods, which require calibration and manual interpretation of results. Machine learning algorithms have also been used in healthcare for various applications, but may not have been specifically applied to pulmonary function testing.

How does this technology compare to traditional spirometry methods in terms of accuracy and efficiency?

This article does not provide a direct comparison between this technology and traditional spirometry methods.

What are the potential limitations or challenges in implementing machine learning algorithms for measuring lung function without spirometer information?

This article does not address the potential limitations or challenges in implementing machine learning algorithms for measuring lung function without spirometer information.

Original Abstract Submitted

Methods and systems for pulmonary function testing of a subject are provided. Aspects of the present invention include methods and systems configured to generate flow volume curves and compute lung function parameters of a subject and determine potential clinical interpretations of pulmonary function. In addition, the present invention offers advantages including (i) measuring lung function without initial calibration of spirometer information, (ii) the ability to use spirometer information to develop a machine learning based algorithm which will eventually measure lung function without needing spirometer information at all, (iii) computing metrics such as chest and waist width and sitting height of subject.