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- GRU.tar.gz +3 -0
- GRU/A1F50A2F50_A3F50/Dy_comp.safetensors +3 -0
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- GRU/A1F50_A2F50/Dy_comp.safetensors +3 -0
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- GRU/A1F50_A2F50/P_mean.safetensors +3 -0
- GRU/A1F50_A2F50/P_peak.safetensors +3 -0
- GRU/A1F50_A3F50/Dy_comp.safetensors +3 -0
- GRU/A1F50_A3F50/Ex_vol.safetensors +3 -0
- GRU/A1F50_A3F50/P_mean.safetensors +3 -0
- GRU/A1F50_A3F50/P_peak.safetensors +3 -0
- README.md +173 -0
- XGB.tar.gz +3 -0
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README.md
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---
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license: cc-by-nc-nd-4.0
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language:
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- en
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tags: [digital-twin, ex vivo lung, time-series, forecasting, regression]
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---
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<h1 align="center">
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<span style="font-size:32px">
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Digital Twins of Ex Vivo Human Lungs
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</span>
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</h1>
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<p align="center">
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<a href="https://your-project-page-url">
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<img alt="Project Page"
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src="https://img.shields.io/badge/Project-Page-blue" />
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</a>
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<a href="https://github.com/Sage-Lab-ai/DT_Lung">
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<img alt="GitHub Code"
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src="https://img.shields.io/badge/GitHub-Code-black?logo=github" />
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</a>
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<a href="https://colab.research.google.com/drive/1IrzccQ09mP5amxQTZy07n4LgLSC2r1uj?usp=sharing">
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<img alt="Colab: DT Lung Demo"
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src="https://img.shields.io/badge/Colab-DT%20Lung%20Demo-orange?logo=google-colab" />
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</a>
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<a href="https://dt-lung.streamlit.app/">
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<img alt="Streamlit Web App"
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src="https://img.shields.io/badge/Streamlit-Web%20App-red?logo=streamlit" />
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</a>
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<a href="https://huggingface.co/SageLabUHN/DT_Lung">
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<img alt="Hugging Face Model"
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src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface" />
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</a>
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<a href="https://huggingface.co/datasets/SageLabUHN/DT_Lung_Demo_Data">
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<img alt="Hugging Face Dataset"
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src="https://img.shields.io/badge/HuggingFace-Dataset-yellow?logo=huggingface" />
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</a>
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</p>
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## Purpose
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This is the official model repository that accompanies [Digital Twin of Ex Vivo Human Lungs Project](https://github.com/Sage-Lab-ai/DT_Lung). This repository contains *a full collection of multi-modal models for creating digital twins of ex vivo human lungs*.
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**Authors**
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<div align="center">
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<span style="font-size:16px">
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Xuanzi Zhou MHSc¹⁻³, Bo Wang PhD⁴⁻⁷, Yiyang Wei BCS¹⁻³, Serena Hacker MScAC<sup>1,2,4</sup>, Sumin Kim MSc<sup>1,2,4</sup>,
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Thomas Borrillo MHSc<sup>1,2</sup>, Abby McCaig BSc¹⁻³, Haaniya Ahmed<sup>1,2</sup>, Youxue Ren MSc<sup>1,2</sup>,
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Olivia Hough PhD<sup>1,2</sup>, Luca Orsini MD<sup>1,2,8</sup>, Bonnie T. Chao PhD<sup>1,2</sup>, Micheal McInnis MD⁹,
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Marcelo Cypel MD<sup>1-3,10</sup>, Mingyao Liu MD<sup>1-3,10</sup>, Jonathan C. Yeung MD<sup>1-3,10</sup>,
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Lorenzo Del Sorbo MD<sup>1,2,8</sup>, Shaf Keshavjee* MD<sup>1-3,10</sup>, and Andrew T. Sage* PhD<sup>1-3,10</sup>
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</span>
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</div>
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<div align="center">
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<span style="font-size:14px">
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1. Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada <br>
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2. Toronto Lung Transplant Program, Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada <br>
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3. Institute of Medical Science, University of Toronto, Toronto, ON, Canada <br>
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4. Department of Computer Science, University of Toronto, Toronto, ON, Canada <br>
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5. Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada <br>
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6. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada <br>
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7. Vector Institute, Toronto, ON, Canada <br>
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8. Interdepartmental Division of Critical Care Medicine, Medical and Surgical Intensive Care Unit, University Health Network, Toronto, ON, Canada <br>
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9. Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, University Health Network, Toronto, ON, Canada <br>
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10. Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada <br>
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</span>
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</div>
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<div align="center">
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<small>*Co-Senior authors</small>
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</div>
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---
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## Highlights
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- ✅ Trained on the world's largest ex vivo lung function dataset
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- ⚡ Fast inference: Creating a digital lung within minutes
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- 📊 Predicts over 75 multi-modal lung functional parameters including lung physiology, biochemistry, images, protein and transcriptomic markers.
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- 🔄 Supports both static (single‑timepoint) and dynamic (continuous recalibration) forecasting
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---
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## Available Models
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The DT model is built using two machine learning model architectures: gated recurrent unit (GRU) and XGBoost (XGB).
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1. **GRU‑based Time‑Series Models**
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- `DT_Lung/GRU`
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- `A1F50_A2F50` – Collection of GRU models for static digital lung forecasting (Forecast 2<sup>nd</sup> hour lung function using 1<sup>st</sup> hour baseline data)
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- `A1F50_A3F50` – Collection of GRU models for static digital lung forecasting (Forecast 3<sup>rd</sup> hour lung function using 1<sup>st</sup> hour baseline data)
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- `A1F50A2F50_A3F50` – Collection of GRU models for dynamic digital lung forecasting (Forecast 3<sup>rd</sup> hour lung function using 1<sup>st</sup> and 2<sup>nd</sup> hour observed data)
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Legend: `A = assessment period`, `F = first breaths`, `numbers = the number of breaths included` <br>
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*Note: everything before _ is included as input to the model, and everything after _ is forecasted by the model.* <br>
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Each folder contains the best-performing GRU models resulting from our hyperparameter tuning for the specified digital lung setup.
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2. **XGBoost Tabular Regressors**
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Each folder below contains the best-performing XGBoost models resulting from our hyperparameter tuning for the specified lung function parameters for each digital lung setup.
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- `DT_Lung/XGB` – Collection of XGBoost models for multiple data modalities
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- `Hourly` – XGBoost models for predicting hourly lung functional parameters (e.g., oxygenation level, compliance, pH, etc.)
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- `H1_to_H2`: Models for static digital lung forecasting (Forecast 2nd hour lung function using 1st hour baseline data)
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- `H1_to_H3`: Models for static digital lung forecasting (Forecast 3rd hour lung function using 1st hour baseline data)
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- `H1_H2_to_H3`: Models for dynamic digital lung forecasting (Forecast 3rd hour lung function using 1st and 2nd hour observed data)
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- `Protein` – XGBoost models for predicting protein markers (e.g., interleukin-8, interleukin-6, etc.)
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- `H1_to_H2`: Models for static digital lung forecasting (Forecast 2nd hour lung function using 1st hour baseline data)
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- `H1_to_H3`: Models for static digital lung forecasting (Forecast 3rd hour lung function using 1st hour baseline data)
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- `H1_predH2_to_H3`: Models for static digital lung forecasting (Forecast 3rd hour lung function using 1st hour baseline data and predicted 2nd hour data)
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- `H1_H2_to_H3`: Models for dynamic digital lung forecasting (Forecast 3rd hour lung function using 1st and 2nd hour obsereved data)
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- `Transcriptomics` – XGBoost models for predicting transcriptomic pathways (e.g., TGF-β, apoptosis, etc.)
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- `static_forecasting`: Models for static digital lung forecasting of gene enrichment scores (Forecast post-perfusion gene enrichment scores using baseline data)
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- `dynamic_forecasting`: Models for dynamic digital lung forecasting of gene enrichment scores (Forecast post-perfusion gene enrichment scores using hourly observed data)
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- `DT_Lung/XGB_PC` – Collection of XGBoost models for lung x-ray images
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121 |
+
- `models_static` – XGBoost models to create static digital lung image features (Forecast 3<sup>rd</sup> hour lung X-ray derived features using 1<sup>st</sup> hour baseline data)
|
122 |
+
- `models_dynamic` – XGBoost models to create dynamic digital lung image features (Forecast 3<sup>rd</sup> hour lung X-ray derived features using 1<sup>st</sup> and 2<sup>nd</sup> hour observed data)
|
123 |
+
|
124 |
+
3. Folders with the `.tar.gz` extension are provided to make model download and distribution easier. Their contents are identical to those in the corresponding folders without the `.tar.gz` extension.
|
125 |
+
|
126 |
+
---
|
127 |
+
|
128 |
+
## Downloading Models
|
129 |
+
|
130 |
+
### Option 1: Download individual models
|
131 |
+
|
132 |
+
```
|
133 |
+
from huggingface_hub import hf_hub_download
|
134 |
+
|
135 |
+
gru_model_dir = hf_hub_download(
|
136 |
+
repo_id="SageLabUHN/DT_Lung",
|
137 |
+
filename="GRU/A1F50_A2F50/Dy_comp.pt", # example path, please modify accordingly
|
138 |
+
local_dir="DT_Lung/models" # example path, specify your local directory
|
139 |
+
)
|
140 |
+
|
141 |
+
print(f"Model downloaded to: {gru_model_dir}")
|
142 |
+
```
|
143 |
+
|
144 |
+
### Option 2: Download all models to a specific folder
|
145 |
+
|
146 |
+
```
|
147 |
+
from huggingface_hub import snapshot_download
|
148 |
+
|
149 |
+
model_dir = snapshot_download(
|
150 |
+
repo_id="SageLabUHN/DT_Lung",
|
151 |
+
local_dir="DT_Lung/Model", # make sure the Model folder is in the project root dir
|
152 |
+
)
|
153 |
+
|
154 |
+
print(f"Models downloaded to: {model_dir}")
|
155 |
+
```
|
156 |
+
|
157 |
+
Alternatively, you can manually download the models from the [Hugging Face repository page](https://huggingface.co/SageLabUHN/DT_Lung/tree/main).
|
158 |
+
|
159 |
+
## Intended Use
|
160 |
+
|
161 |
+
**Intended for**
|
162 |
+
- Research and education use
|
163 |
+
|
164 |
+
**Not for**
|
165 |
+
- Clinical decisions without further validation
|
166 |
+
- Commercial deployment (non‑commercial only)
|
167 |
+
|
168 |
+
---
|
169 |
+
|
170 |
+
## License
|
171 |
+
|
172 |
+
This repository and all model weights are released under the **Creative Commons Attribution‑NonCommercial‑ShareAlike 4.0 International (CC BY‑NC‑SA 4.0)** license.
|
173 |
+
Commercial use is **prohibited**.
|
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