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| title: ECG to ECHO Screening AI | |
| emoji: π« | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: streamlit | |
| sdk_version: 1.39.0 | |
| python_version: "3.10" | |
| app_file: app.py | |
| pinned: false | |
| license: cc-by-nc-4.0 | |
| # π« ECG β ECHO Clinical Screening AI | |
| AI-powered screening tool that predicts **Left Ventricular Dysfunction** and **Regional Wall Motion Abnormalities** from a single 12-lead ECG image. Designed for rural cardiac screening in resource-limited settings. | |
| ## How it works | |
| A 5-fold ensemble of EfficientNet-B3 (NoisyStudent pretrained) models analyzes the ECG image and outputs: | |
| 1. **EF Risk** β likelihood of reduced ejection fraction (< 55%) | |
| 2. **RWMA Risk** β likelihood of regional wall motion abnormality | |
| ## Training | |
| - **Dataset:** 500 paired ECG-ECHO records from cardiology clinic in Karnataka, India | |
| - **Validation:** 5-fold Multilabel Stratified K-Fold cross-validation | |
| - **Backbone:** EfficientNet-B3 with NoisyStudent pretraining | |
| ## β οΈ Disclaimer | |
| This is a **research prototype** for screening purposes only. NOT approved for clinical diagnostic use. All predictions must be reviewed by a qualified cardiologist. |