Oxynet

Oxynet is a powerful AI-driven toolset for the automatic interpretation of cardiopulmonary exercise test (CPET) data.

💻 Try the web app

About the Oxynet Project

Universal access to high-quality healthcare remains a global challenge. Oxynet leverages AI and vast data resources to revolutionize the diagnosis of medical conditions through CPET analysis, enabling accurate and timely clinical decisions while reducing costs associated with diagnostic errors and delays.

Oxynet combines three key components:

We actively seek collaboration with universities, hospitals, clinics, medical professionals, and companies involved in medical device development and commercialization. Together, we can advance research and development, provide financial support for research initiatives, contribute to scientific publications, share valuable data, develop innovative web applications, conduct comprehensive market analyses, and validate algorithms for clinical implementation.

The Pyoxynet Package

Pyoxynet is a comprehensive suite of deep neural network algorithms specifically designed for CPET data analysis. Built using Keras and TensorFlow, the models are available in efficient TFLite format, with direct TensorFlow model usage supported from version 11.6 onward.

The package includes two primary models:

🐍 Install the Python package
📁 Read the docs

Installation

Pyoxynet requires Python 3.8 or higher. To install the package, run:

pip install pyoxynet

Or:

pip install git+https://github.com/andreazignoli/pyoxynet.git#subdirectory=pyoxynet

Usage

To use the inference model, provide CPET data including VO₂, VCO₂, VE, PetO₂, PetCO₂, VE/VO₂, and VE/VCO₂. Pyoxynet automatically handles data interpolation and supports multiple data formats: second-by-second, breath-by-breath, and averaged data.

Basic Example

import pyoxynet

# Load the TFL model
tfl_model = pyoxynet.load_tf_model()

# Make inference on a random input
test_tfl_model(tfl_model)

# Plot the inference on a test dataset
pyoxynet.test_pyoxynet()

Data Generation

Pyoxynet includes a Conditional Generative Adversarial Network (CGAN) capable of generating realistic CPET data for research and testing purposes.

Generation Example

from pyoxynet import *

# Call the generator
generator = load_tf_generator()

# Generate a Pandas df with fake CPET data
df = generate_CPET(generator, plot=True)

# Call Oxynet for inference on fake data
test_pyoxynet(input_df=df)

The generated synthetic data includes all essential CPET parameters: VO₂, VCO₂, VE, HR, RF, PetO₂, and PetCO₂.

Contact Information

📧 Feedback & Issues: oxynetcpetinterpreter@gmail.com
📧 Principal Investigator: Andrea Zignoli (andrea.zignoli@unitn.it)

Scientific Publications

Explore the peer-reviewed research and publications behind Oxynet:

Acknowledgments

We extend our gratitude to the following resources and contributors:

Disclaimer

All content on this website, including text, images, tables, and other materials, is provided for informational purposes only. The information and software tools provided here are not substitutes for professional medical advice, diagnosis, or treatment. Always consult your physician or other qualified healthcare provider with any questions regarding a medical condition. Never disregard professional medical advice or delay seeking it based on information provided by this software.