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Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long

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Artificial intelligence (AI) models can now estimate and forecast tissue hysteresivity, a key marker for respiratory diseases, using combined data from lung function tests and wearable sensors. This approach significantly reduces required measurements, minimizing patient effort and costs.

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artificial intelligencecontinuous monitoringelectrocardiogramestimationlong short-term memory (LSTM)low-frequency oscillation techniquelung function testrespiratory mechanicstime-series forecasting

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Area of Science:

  • Respiratory Medicine
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Tissue hysteresivity is a crucial indicator for respiratory disease progression, typically measured via forced oscillation techniques.
  • Current measurement protocols can be lengthy and demanding for patients.
  • Utilizing multivariate data and AI for respiratory monitoring is an emerging area with untapped potential.

Purpose of the Study:

  • To reduce the number and duration of measurements for tissue hysteresivity.
  • To explore the use of AI, specifically Long Short-Term Memory (LSTM) networks, for estimating and forecasting tissue hysteresivity.
  • To integrate data from Forced Oscillation Technique (FOT) devices and the Equivital (EQV) LifeMonitor for enhanced respiratory analysis.

Main Methods:

  • Collected synchronized data from EQV LifeMonitor, FOT prototype, and RESMON devices over a two-hour protocol.
  • Employed LSTM models to estimate hysteresivity (η) using continuous heart rate (HR) data.
  • Utilized LSTM to forecast η by first predicting HR from electrocardiogram (ECG) data.

Main Results:

  • LSTM accurately estimated tissue hysteresivity (η) with R2 = 0.851 and MSE = 0.296.
  • LSTM successfully forecasted η with R2 = 0.883 and MSE = 0.528.
  • The number of required measurements was reduced by a factor of three, from ten to three.

Conclusions:

  • AI-driven integration of FOT and wearable sensor data offers a promising method for accurate tissue hysteresivity estimation and forecasting.
  • This novel approach significantly reduces patient burden, measurement time, and associated costs.
  • The study highlights the potential of AI in advancing ambulatory respiratory monitoring.