README.md

metadata

title: Angio AI
emoji: 🫀
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: apache-2.0

Angio AI — Coronary Angiography Analysis System

MS Data Science Thesis · Information Technology University Lahore
Supervisor: Dr. Arif Mehmood

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What it does

Upload a coronary X-ray angiography (XCA) video and the pipeline automatically:

Step	Module	Output
1	Keyframe extraction	Best diagnostic frame (contrast × sharpness score)
2	Mask2Former	Stenosis detection — bounding boxes + instance masks
3a	ResUNet	Binary vessel mask (Dice 0.8015 on ARCADE)
3b	YOLOv8m-seg	26-class coronary anatomy segmentation
4	FFR Pipeline v4	Quantitative Flow Ratio (QFR) estimation
5	SYNTAX Score	Lesion complexity scoring

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Models

All checkpoints are stored in MuhammadAdil63/angio-ai-checkpoints and downloaded automatically on first run.

File	Architecture	Task	Performance
mask2former_best.pth	Mask2Former Swin-Base	Stenosis detection	—
binary_best.pth	ResUNet (16→256 ch)	Binary vessel segmentation	Dice 0.8015
best.pt	YOLOv8m-seg (nc=26)	26-class coronary anatomy	—

Dataset: ARCADE Challenge (syntax + stenosis splits)

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Notes

• Hardware: CPU-only (HF free tier) — inference takes ~30–60 seconds per video
• Input: MP4 / AVI coronary angiography video, ideally ≥5 seconds
• Scale: Default 3.75 px/mm (ARCADE hardware). Adjust slider for non-ARCADE data.
• FFR formula: QFR = 1 − (0.33·DS + 0.60·DS²) — Tu et al. JACC 2016 / FAVOR II

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This tool is for research purposes only and is not intended for clinical use.