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Related Experiment Video

Updated: Jun 22, 2025

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Published on: September 16, 2019

Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in

Pranai Tandon¹, Kim-Anh-Nhi Nguyen², Masoud Edalati²

¹Department of Medicine Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Bioengineering (Basel, Switzerland)

|June 27, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning model using chest X-rays to predict extubation success in mechanically ventilated patients. The AI model shows improved performance over traditional methods, aiding clinical decisions.

Keywords:

artificial intelligence clinical decision support deep learning machine learning mechanical ventilation respiratory failure transfer learning ventilator liberation

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Published on: October 10, 2018

Area of Science:

Critical Care Medicine
Artificial Intelligence in Healthcare
Medical Imaging Analysis

Background:

Extubation readiness assessment is crucial but challenging for clinicians.
Existing machine learning models using tabular data have limitations in capturing complex patient information.
Chest X-rays offer rich, routinely collected data for predicting extubation outcomes.

Purpose of the Study:

To develop and validate a deep learning model for predicting extubation success using chest X-rays.
To compare the model's performance against established clinical indices and prior studies.
To leverage advanced AI techniques for improved patient management in intensive care.

Main Methods:

A deep learning model (ResNet50) was trained on chest X-rays from 2288 mechanically ventilated patients.
Transfer learning and k-fold cross-validation were employed for model training and validation.
Ensemble methods and Grad-CAM visualization were used to refine and interpret the model.

Main Results:

The deep learning model achieved an AUC of 0.66, outperforming the Rapid Shallow Breathing Index (AUC 0.61) and a previous study (AUC 0.55).
The model demonstrated a sensitivity of 0.62 and specificity of 0.60.
Image analysis highlighted specific regions in chest X-rays influencing the model's predictions.

Conclusions:

Deep learning models utilizing chest X-rays show promise in predicting extubation success.
This approach offers a potential improvement over current clinical assessment methods.
Further research and model refinement are warranted to enhance predictive accuracy.