---
title: DRIL OCT Classification
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "5.29.0"
python_version: 3.11
app_file: app.py
pinned: false
license: mit
---

# DRIL OCT Classification Demo

This Space provides an interactive demo for **Diabetic Retinopathy-related Inner Layer (DRIL)** detection from OCT B-scan images using fine-tuned deep learning models trained in the DRIL Classification Benchmark.

## What is DRIL?

DRIL (Disruption of Retinal Inner Layers) refers to the loss of distinction between the inner plexiform, inner nuclear, and outer plexiform layers on OCT imaging. It is a recognized prognostic biomarker in diabetic macular edema associated with poor visual outcomes after treatment.

## Models Available

| Model | Architecture | AUC (CV) |
|---|---|---|
| RETFound (Moderate FT) | Vision Transformer | Reported in paper |
| RETFound (Conservative FT) | Vision Transformer | Reported in paper |
| DenseNet-121 | CNN | Reported in paper |
| EfficientNet-B0 | CNN | Reported in paper |

## How to Use

1. Upload a fovea-centered OCT B-scan image (JPEG or PNG).
2. Select the model you wish to use.
3. Click "Classify" to get the DRIL probability and predicted label.

## Notes

- The model was trained on a private OCT dataset (429 DRIL, 394 No-DRIL cases).
- Input images should be macular OCT B-scans. The model performs best on images similar to the training distribution.
- Test-time augmentation (TTA) is applied for more robust predictions.
- This demo is for research purposes only and should not be used for clinical decision-making.

## Source Code and Weights

- GitHub: https://github.com/ai-research-2025/dril-classification-benchmark
- Model weights: https://drive.google.com/drive/folders/1MaenUEydngBCDaa-WqbwX8cGv5EzpYR_