What is EchoForge?

EchoForge is a modular library of pre-trained deep learning models purpose-built for echocardiographic image analysis. Developed by the Intelligent Sensing and Vision Lab at The University of West London, it offers clinicians, researchers, and developers immediate access to reusable AI tools that support and accelerate cardiac ultrasound workflows.

By streamlining repetitive tasks and promoting reproducible methods, EchoForge reduces duplication of effort and lowers the barrier for integrating artificial intelligence into clinical, educational, and research-focused echocardiography.

Why EchoForge?

EchoForge is designed to:

  • 🩺 Reduce manual workload in echo interpretation
  • 📚 Promote standardised workflows across hospitals and academic settings
  • 🎓 Support trainees and junior sonographers with objective feedback
  • ⚙️ Accelerate AI research with ready-to-deploy, reproducible models

It bridges the gap between cutting-edge machine learning and real-world echocardiographic applications — all without requiring you to build models from scratch.

Who Is It For?

EchoForge is designed for:

  • Clinicians & Sonographers — looking to automate or validate their workflow
  • Clinical Researchers — building reproducible and explainable AI pipelines
  • Medical Educators — developing intelligent training or feedback systems
  • Engineers & Developers — prototyping or benchmarking echo-related tools

Whether you’re on the frontlines of care or working behind the scenes in data science, EchoForge adapts to your environment.

Real‑World Applications

  • 🩺 View Classification: Automatically identify standard echo views (e.g. PLAX, A4C, PSAX) from still images or video frames.
  • ✏️ Segmentation: Delineate cardiac structures such as the left ventricle or myocardium in near‑real time.
  • ⏱️ Phase Detection: Automatically detect cardiac cycle phases (e.g. systole and diastole) from dynamic echo sequences.
  • 🔍 Image Quality Assessment (in development): Assist sonographers by scoring image quality for clarity and diagnostic suitability.
  • 📈 Trainee Feedback (experimental): Use AI to support skill development over time with objective feedback.

These tools are designed to be lightweight and developer-friendly — suitable for integration into clinical research pipelines, educational tools, or engineering workflows. A basic understanding of Python is required to use and customise the models effectively.

Built with Best Practices

EchoForge is constructed using principled AI software design:

  • Modular: Each task-specific model is self-contained and interchangeable.
  • Scalable: New models and datasets can be added seamlessly.
  • Reproducible: Models are trained on documented datasets with transparent evaluation metrics.
  • Interoperable: Built on TensorFlow/Keras and designed to plug into existing annotation and diagnostic tools.

Clinicians may use pretrained models directly for inference, while developers can integrate and fine‑tune them in broader AI pipelines.

Explore Further

Interested in diving deeper? Visit our GitHub repository to see full model specifications, usage examples, benchmark results and contribution guidelines:

👉 EchoForge on GitHub

Acknowledgements

EchoForge is supported by:

  • The Intelligent Sensing and Vision Lab, University of West London
  • Thrive Research Centre
  • The IntSaV research group

We welcome collaboration with clinicians, researchers and developers to enhance and extend the EchoForge platform.