Spaces:
Running
Running
Ashkan Taghipour (The University of Western Australia)
Fix pydantic/Gradio compatibility issue causing 'No API found' error
c29becc | title: HeartWatchAI | |
| emoji: ❤️ | |
| colorFrom: red | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 5.6.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: AI-powered 12-Lead ECG Analysis | |
| hf_oauth: false | |
| # HeartWatch AI | |
| AI-powered 12-Lead ECG analysis using deep learning models. | |
| ## Features | |
| - **77-Class ECG Diagnosis**: Detect 77 different cardiac conditions | |
| - **LVEF Prediction**: Predict left ventricular ejection fraction < 40% and < 50% | |
| - **AFib Risk**: 5-year atrial fibrillation risk prediction | |
| - **Interactive Visualization**: Clinical 4x3 lead layout with ECG paper grid | |
| ## Models | |
| This demo uses EfficientNetV2 models from the DeepECG project: | |
| - `heartwise/EfficientNetV2_77_Classes` | |
| - `heartwise/EfficientNetV2_LVEF_40` | |
| - `heartwise/EfficientNetV2_LVEF_50` | |
| - `heartwise/EfficientNetV2_AFIB_5y` | |
| ## Input Format | |
| - NumPy array (.npy file) | |
| - Shape: (2500, 12) or (12, 2500) | |
| - 12 standard leads: I, II, III, aVR, aVL, aVF, V1-V6 | |
| - 10 seconds at 250 Hz sampling rate | |
| ## Disclaimer | |
| This is a research demonstration tool. Predictions should NOT be used for clinical decision-making. Always consult qualified healthcare professionals for medical advice. | |