Remote, Depth-Based Lung Function Assessment.

Area of Science:

• Biomedical Engineering
• Respiratory Medicine
• Medical Imaging

Background:

• Pulmonary function testing (PFT) is crucial for diagnosing and managing respiratory diseases.
• Traditional spirometry methods are often cumbersome, requiring direct patient contact and specialized equipment.
• There is a need for noninvasive, remote, and accessible PFT solutions.

Purpose of the Study:

• To develop and validate a remote, noninvasive pulmonary function testing (PFT) approach using depth sensor technology.
• To assess the accuracy of the proposed method by comparing its results to traditional spirometry.

Main Methods:

• A 3D chest model was constructed from depth sensor data to estimate chest volume variations.
• Volume-time and flow-time data were generated for forced vital capacity (FVC) and slow vital capacity (SVC) tests.
• Eleven key PFT measures were computed and validated against spirometer readings in 85 patients.

Main Results:

• The depth-based method demonstrated high correlation with spirometer measurements for both FVC and SVC.
• Intra-test and intra-subject measures showed excellent agreement between the proposed technique and traditional spirometry.
• The system successfully computed eleven clinical PFT measures with high accuracy.

Conclusions:

• The proposed depth-based approach provides an accurate, low-cost, and remote alternative for pulmonary function testing.
• The findings establish a linear relationship between chest surface motion and lung volume changes.
• This technology has the potential to significantly improve accessibility to PFT.