Erdes Oculardetachment Eval

---
name: erdes-oculardetachment-eval
description: Evaluates the ability of spatiotemporal deep learning models to classify ocular ultrasound videos into binary diagnostic categories: detecting retinal detachment (RD) versus non-RD, and classifying macular status (intact vs. detached). It probes the model's capacity to learn subtle spatiotemporal patterns in medical ultrasound while handling real-world class imbalance. Use when the user wants to benchmark on ERDES, or asks about evaluating this task. Reports F1-Score.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2508.04735
  bibtex_key: navard2025erdes
  confidence: high
---

# erdes-oculardetachment-eval

> ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound — Navard et al. (2025) (arXiv:2508.04735, 2025)

## What this evaluates

Evaluates the ability of spatiotemporal deep learning models to classify ocular ultrasound videos into binary diagnostic categories: detecting retinal detachment (RD) versus non-RD, and classifying macular status (intact vs. detached). It probes the model's capacity to learn subtle spatiotemporal patterns in medical ultrasound while handling real-world class imbalance.

## Datasets

- **ERDES** — total 5381; splits: train (3413), val (377), test (945); repo https://github.com/osupcvlab/ERDES

## Metrics

- `Accuracy` — range: [0, 1]
  - Proportion of true results (both true positives and true negatives) among all predictions. Formula: (TP + TN) / (TP + TN + FP + FN).
- `Precision` — range: [0, 1]
  - Proportion of predicted positive cases that are actually positive. Formula: TP / (TP + FP).
- `Sensitivity` — range: [0, 1]
  - Proportion of actual positive cases correctly identified. Formula: TP / (TP + FN).
- `Specificity` — range: [0, 1]
  - Proportion of actual negative cases correctly identified. Formula: TN / (TN + FP).
- `F1-Score` **(primary)** — range: [0, 1]
  - Harmonic mean of precision and recall. Formula: 2 * (Precision * Recall) / (Precision + Recall).

## Input / output format

**Input**: Preprocessed ocular ultrasound video clips (MP4) cropped to the region of interest (ROI) using YOLOv8, padded to dimensions divisible by 16, and fed as spatiotemporal volumes to the model.

**Output**: Binary classification probability score (e.g., P(RD) or P(Macula_Detached)), thresholded at 0.5 to yield a discrete class label (0 or 1).

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
    tn = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 0)
    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
    accuracy = (tp + tn) / (tp + tn + fp + fn)
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
    sensitivity = tp / (tp + fn) if (tp + fn) > 0 else 0.0
    specificity = tn / (tn + fp) if (tn + fp) > 0 else 0.0
    f1 = 2 * precision * sensitivity / (precision + sensitivity) if (precision + sensitivity) > 0 else 0.0
    return {'accuracy': accuracy, 'precision': precision, 'sensitivity': sensitivity, 'specificity': specificity, 'f1': f1}
```

## Common pitfalls

- Severe class imbalance exists (Non-RD: 4,233 vs RD: 502), requiring stratified splits and careful metric selection.
- Posterior vitreous detachment (PVD) clips are explicitly excluded from the Non-RD vs. RD binary task.
- Anatomical subclasses (TD, ND, Bilateral) are grouped under the main RD or Macula labels rather than evaluated separately.
- Models are trained from scratch without external pretraining, which may limit performance compared to pretrained baselines.

## Evidence (verbatim from paper)

> Performance was evaluated using five standard classification metrics, namely Accuracy, Precision, Sensitivity (Recall), Specificity, and the F1-Score. These metrics provide complementary insights into the performance of model classification, especially in the context of imbalanced datasets.

## Citation

```bibtex
@misc{navard2025erdes,
  title={ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound},
  author={Navard et al. (2025)},
  year={2025},
  note={arXiv:2508.04735}
}
```

- arXiv: 2508.04735