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Related Concept Videos

Pulmonary Function Tests01:25

Pulmonary Function Tests

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Pulmonary Function Tests (PFTs)
Pulmonary Function Tests are crucial diagnostic tools for assessing respiratory function, particularly in patients with chronic respiratory disorders. They comprehensively evaluate lung volumes, ventilatory function, breathing mechanics, diffusion, and gas exchange. These tests help diagnose pulmonary diseases and play a significant role in monitoring disease progression, evaluating disability, and assessing response to therapy.
PFTs involve using a spirometer, a...
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Assessment of Respiration01:23

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
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Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression.

Luay Fraiwan1,2, Natheer Khasawneh3, Khaldon Lweesy2

  • 1Department of Electrical, Computer and Biomedical Engineering, Abu Dhabi University, Abu Dhabi 55991, United Arab Emirates.

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Summary

This study shows non-contact spirometry is feasible using mobile thermal imaging. This technology can monitor respiratory diseases by measuring breathing rate and air volume without physical contact.

Keywords:
artificial intelligence regressionnon-contact spirometryrespiration rate mobile applicationrespiration signalthermal camera

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Respiratory Physiology

Background:

  • Non-contact physiological monitoring is crucial for disease management.
  • Conventional spirometry requires physical contact, limiting its use in certain scenarios.
  • Thermal imaging offers a potential non-contact method for physiological measurements.

Purpose of the Study:

  • To assess the feasibility of non-contact spirometry using a mobile thermal imaging system.
  • To develop a system for measuring respiration rate and air volume without physical contact.
  • To evaluate the performance of artificial intelligence models in predicting inhalation and exhalation volumes.

Main Methods:

  • Acquired thermal images from 19 subjects to capture respiratory patterns.
  • Developed a mobile application for real-time respiration rate measurement and data export.
  • Utilized the OpenCV library for identifying the nose and mouth regions in thermal images.
  • Employed artificial intelligence regressors (Random Forest, Adaptive Boosting, Gradient Boosting, Decision Trees) to predict air volume.

Main Results:

  • The decision tree regressor achieved an R-square value of 0.9998 and a mean square error of 0.0023 for air volume prediction.
  • Four AI regressors demonstrated excellent performance in predicting inhalation and exhalation volumes.
  • The system successfully measured respiration rate and estimated air volume non-contact.

Conclusions:

  • Non-contact spirometry using a mobile thermal imaging system is a feasible approach.
  • This technology can perform basic spirometry measurements comparable to conventional devices.
  • The developed system holds potential for acute and chronic pulmonary disease monitoring and diagnosis.