---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: image_id
    dtype: string
  - name: image_path
    dtype: string
  - name: dr_grade
    dtype: int64
  - name: dr_grade_name
    dtype: string
  - name: age
    dtype: float64
  - name: sex
    dtype: string
  - name: duration_years
    dtype: float64
  - name: synthetic
    dtype: bool
  - name: image_population
    dtype: string
  - name: ehr_params_version
    dtype: string
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 157322831.407
    num_examples: 2487
  - name: validation
    num_bytes: 34207443.0
    num_examples: 532
  - name: test
    num_bytes: 34229290.0
    num_examples: 535
  download_size: 227010357
  dataset_size: 225759564.407
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
source_datasets:
- aptos2019-blindness-detection
pretty_name: 'DR-Grading: Fundus Diabetic-Retinopathy Grading with Africa-Grounded
  Synthetic Context'
language:
- en
task_categories:
- image-classification
tags:
- medical
- healthcare
- ophthalmology
- diabetic-retinopathy
- fundus
- retinal-imaging
- multimodal
- synthetic-data
- africa
- global-health
size_categories:
- 1K<n<10K
annotations_creators:
- machine-generated
license: apache-2.0
---
# DR-Grading — Fundus Diabetic-Retinopathy Grading with Africa-Grounded Synthetic Clinical Context

*A dataset for cross-sectional 5-class diabetic-retinopathy grading, pairing
resized colour fundus photographs with minimal, epidemiology-grounded synthetic
point-of-care context (age, sex, diabetes duration).*

**Version 1.0.0** · core `dr_synth` 1.0.0 · part of the **DR-Africa** dataset family
(see also [`dr-progression`](../dr-progression) and
[`dr-africa-benchmark`](../dr-africa-benchmark)).

---

## Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness, and its
burden is rising fastest in low- and middle-income settings, including much of
Sub-Saharan Africa (SSA), where an estimated 35% of people living with diabetes
have some DR and ~10% have vision-threatening disease [1]. Most publicly
available DR-grading datasets, however, are collected in South Asian, North
American, or European populations and provide images *only* — with no clinical
context to support the multimodal models that modern screening increasingly
relies on. **DR-Grading** is a methods-development dataset that pairs 3,554
colour fundus photographs, each labelled with the five-stage international DR
scale, with a **minimal synthetic clinical context** (age, sex, diabetes
duration) whose statistical structure is grounded in published SSA diabetes
epidemiology. The dataset is designed for cross-sectional grading: predicting
DR stage from a fundus image, optionally conditioned on the limited information
available at the point of grading. We document the construction, the grounding
literature, and a reproducible **distribution audit** that validates the
synthetic context against its source effect sizes. Two boundaries should be
clear: the images are not African, and the clinical context is synthetic and not
patient-linked. The dataset is meant for building and testing grading pipelines
and for teaching, not as a source of clinical or epidemiological findings.

---

## 1. Introduction

Automated DR grading from colour fundus photography is among the most mature
applications of medical computer vision, with regulator-cleared systems already
deployed for screening. Yet the datasets that underpin this progress are
geographically narrow. The two largest open corpora — EyePACS (United States)
and APTOS 2019 (India) — together dominate the literature, and downstream models
inherit their populations' fundus pigmentation, comorbidity patterns, camera
hardware, and care-seeking behaviour. When such models are evaluated on African
populations they frequently degrade, a manifestation of dataset shift that is
both a scientific and an equity problem.

The **DR-Africa** family addresses this gap. Its first objective is a
*multimodal* resource: DR research is moving beyond the image alone toward models
that combine retinal imaging with systemic clinical variables (glycaemic control,
diabetes duration, blood pressure, renal function), which are the main drivers of
incidence and progression. Real linked image–EHR cohorts from Africa are scarce,
so we start with **epidemiology-grounded synthetic** clinical data: synthetic
records whose marginal distributions and conditional structure are taken from
published SSA cohort studies and meta-analyses.

This repository, **DR-Grading**, is the entry point of the family and the
narrowest in scope (separation of concerns). It exposes only the
inputs realistically available *at the moment of grading* — the image plus three
context fields — and the grade label. The richer systemic record lives in
[`dr-progression`](../dr-progression); screening-prevalence evaluation lives in
[`dr-africa-benchmark`](../dr-africa-benchmark).

**Contributions.**
1. A clean, split, audited 5-class DR-grading dataset with a minimal synthetic
   clinical context grounded in SSA epidemiology.
2. A transparent, reproducible generation procedure (seeded; all parameters
   cited).
3. A built-in distribution audit that gates every release on explicit checks.

---

## 2. Background and Related Datasets

**DR grading scale.** The international clinical DR severity scale defines five
stages: no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and
proliferative DR (PDR). These map to the five source folders used here
(`No_DR`, `Mild`, `Moderate`, `Severe`, `Proliferate_DR`).

**Existing DR image datasets.** EyePACS, APTOS 2019, Messidor / Messidor-2,
IDRiD, DDR, and DeepDRiD are the most widely used. They differ in grading
protocol, image quality, and population. None ships paired structured clinical
data, and none is African in origin.

**Epidemiology of DR in Africa.** Pooled SSA estimates place any-DR prevalence
around 30–35% of people with diabetes and PDR at ~1% in population-based studies
[1, 10]. Strong, consistent risk factors include diabetes **duration**, poor
**glycaemic control**, and **hypertension** [2]. Late presentation is a defining
feature of African DR services and correlates with severity at first contact
[10]. These findings are the empirical backbone of the synthetic context here
and in the sibling datasets.

---

## 3. Dataset Construction

### 3.1 Image source

The fundus images are the resized colour photographs from the **APTOS 2019
Blindness Detection** corpus (Aravind Eye Hospital, India) [11], reorganised
into five class folders. All images are 224×224 RGB PNGs. The class counts are
deliberately *referral/clinic-skewed* (see §4) rather than screening-distributed.

> **The images are not African.** They function as a transfer-learning source.
> Models trained here will face domain shift on African fundi, which are more
> pigmented (darker background, altered contrast) and are increasingly captured
> on smartphone-based fundus cameras. See §8 and §9.

### 3.2 Synthetic clinical context

For each image we generate three context fields — `age`, `sex`, and
`duration_years` (diabetes duration) — using a seeded generator
(`dr_synth.generate`). These are the fields plausibly available to a grader and
were chosen to keep the grading task realistic without leaking the label.

Each record is produced from a **per-image deterministic seed** (a hash of the
image id and the global seed), so a given image always yields the same context
regardless of iteration order, machine, or how many records precede it. The
global generation seed is `42`.

### 3.3 Grounding and conditioning

Demographics are drawn grade-independently (age and sex are not significant DR
predictors in the SSA meta-analysis [2]). **Diabetes duration is conditioned on
DR grade**, because duration is the single strongest and most consistent
predictor of DR severity in African cohorts. We use the per-stage duration
gradient reported in the Dakar study [7] — mean duration rising from ~5.3 years
at no-DR to ~16.8 years at proliferative DR — with wide standard deviations that
preserve realistic overlap between adjacent grades. All parameters and their
citations live in [`dr_synth/params.yaml`](dr_synth/params.yaml).

### 3.4 Splits

A stratified 70/15/15 train/validation/test split is provided in the `split`
column and as separate parquet files (`data/train.parquet`,
`data/validation.parquet`, `data/test.parquet`). Stratification is on
`dr_grade`; the split seed is `7`. Splits are assigned at the image level, and
the audit verifies that **no image id appears in more than one split**.

---

## 4. Dataset Statistics

| Field | Type | Description |
|---|---|---|
| `image_id` | string | APTOS image identifier (filename stem) |
| `image_path` | string | path relative to `images_root` (e.g. `No_DR/xxxx.png`) |
| `dr_grade` | int (0–4) | **label**: 0 No_DR · 1 Mild · 2 Moderate · 3 Severe · 4 PDR |
| `dr_grade_name` | string | human-readable grade |
| `age` | float | years (synthetic) |
| `sex` | string | `F`/`M` (synthetic) |
| `duration_years` | float | diabetes duration, years (synthetic, grade-conditioned) |
| `split` | string | `train`/`val`/`test` |
| `synthetic`, `image_population`, `ehr_params_version` | — | provenance |

**Label distribution (n = 3,554).**

| Grade | Count | Proportion |
|---|---|---|
| No_DR | 968 | 27.2% |
| Mild | 527 | 14.8% |
| Moderate | 395 | 11.1% |
| Severe | 575 | 16.2% |
| Proliferate_DR | 1,089 | 30.6% |

**Splits.** train 2,487 (70.0%) · validation 532 (15.0%) · test 535 (15.1%).

**Context summary.** age mean 55.8 (SD 12.0, range 18–90); duration mean 10.9 y
(SD 7.4); sex ~52% female. Mean duration by grade rises monotonically:
5.9 → 7.4 → 8.6 → 12.7 → 16.9 years (Spearman ρ = 0.60 with grade, p < 10⁻³⁰⁰).

---

## 5. Distribution Audit (Validation)

Every build regenerates [`audit/AUDIT.md`](audit/AUDIT.md) (plus CSV tables and
PNG figures) by running `dr_synth.audit`. Each check reports PASS / WARN / FAIL
against an explicit threshold, so the audit can gate a release. The current
verdict is **PASS**. Checks for this dataset:

- **Label distribution** documented against the SSA screening-prevalence target
  (the curated set is intentionally *not* screening-distributed; see
  `dr-africa-benchmark` for prevalence-corrected evaluation).
- **Split integrity** — no cross-split image-id leakage; unique row ids;
  stratification drift between per-split and overall label proportions < 0.03.
- **Numeric validation** — zero out-of-range values for `age` and
  `duration_years`; zero unexpected missing values.
- **Grade conditioning** — Spearman monotonic-trend test confirms
  `duration_years` increases with grade (ρ = 0.60, p ≈ 0).

Figures: `audit/figures/label_distribution.png`,
`audit/figures/conditioning_duration_years.png`.

---

## 6. Loading and Usage

### Hugging Face `datasets`

```python
from datasets import load_dataset
ds = load_dataset("macular/diabetic-retinopathy-grading-africa")   # 3 splits
print(ds["train"][0])
# {'image_id': ..., 'image_path': 'No_DR/xxxx.png', 'dr_grade': 0, ...}
```

The fundus images are **bundled** as a `datasets.Image` feature, so each example
includes the decoded image alongside its label and context — the dataset is
self-contained and renders in the Hub viewer.

### Local / PyTorch

```bash
pip install -r requirements.txt
python build.py                       # regenerate data/ + audit/
python loader.py --split train --limit 3
```

[`loader.py`](loader.py) provides a `torch.utils.data.Dataset` returning
`(image_tensor, context_tensor, grade)`.

---

## 7. Intended Uses and Benchmarks

**Intended.** Cross-sectional DR-grading models (image-only or image+context);
data augmentation studies; transfer-learning and domain-adaptation baselines
toward African deployment; teaching.

**Suggested protocol.** Train on `train`, tune on `validation`, report on `test`
using quadratic-weighted kappa (the APTOS metric) and per-class recall. For a
screening-realistic estimate, additionally evaluate with the prevalence weights
from [`dr-africa-benchmark`](../dr-africa-benchmark).

**Out of scope.** Clinical decision support; epidemiological inference; any claim
about real African patients.

---

## 8. Limitations

1. **Non-African images.** The fundus photographs originate from an Indian
   population; performance here does not transfer directly to African fundi.
2. **Synthetic, non-linked context.** The context fields are generated, not
   measured, and are not tied to the real patient behind each image. A model can
   exploit the *encoded* association between duration and grade, but this is a
   property of the generator, not evidence about patients.
3. **Referral-skewed labels.** The class balance reflects a clinic/referral
   population, not screening; uncorrected accuracy overstates screening utility.
4. **Minimal context.** Labs and comorbidities are not included here (they belong
   to `dr-progression`).

---

## 9. Ethical Considerations and Data Governance

- **Synthetic data are flagged.** Every record carries `synthetic = True`. No
  attempt should be made to interpret a synthetic record as a real individual.
- **Representation.** Calling a dataset "Africa-grounded" while the imagery is
  non-African could mislead, so we state the provenance plainly and treat the
  images only as a transfer-learning source.
- **Data sovereignty.** The move toward real African imaging and linked records
  must follow local consent, ethics approval, and data-sovereignty principles.
  This synthetic dataset is a placeholder, not a substitute.
- **Purpose.** The dataset exists to support DR research relevant to Africa by
  making multimodal work easier to start, not to replace real-world data collection.

---

## 10. Licensing, Access, and Provenance

- **Images:** derived from APTOS 2019 (Aravind Eye Hospital, India), which is
  released under the **Apache License 2.0** — redistribution is permitted with
  attribution. The bundled images are provided under Apache-2.0; see
  [`LICENSE`](LICENSE) and [`ATTRIBUTION.md`](ATTRIBUTION.md). Users should verify
  the source license on the APTOS distribution they rely on [11].
- **Synthetic context, code, and audit:** generated locally and fully
  reproducible from [`dr_synth/params.yaml`](dr_synth/params.yaml) with the fixed
  seeds above; released under Apache-2.0.
- **Provenance fields:** `image_population = "APTOS-2019-India"`,
  `ehr_params_version`, and the generation seed are recorded on every row.

---

## 11. Maintenance and Versioning

The dataset is fully regenerable: `python build.py`. The grounding parameters and
the shared generator/audit core (`dr_synth`) are version-stamped; any change bumps
`dr_synth.__version__` and `params.yaml meta.version`, which propagate to the
`ehr_params_version` recorded in the data. Issues and corrections to the grounding
literature are welcome.

---

## 12. References

1. *Diabetic retinopathy in Sub-Saharan Africa: prevalence and regional variations — a systematic review and meta-analysis.* BMC Ophthalmology, 2025. https://link.springer.com/article/10.1186/s12886-025-04589-5
2. *Prevalence of diabetic retinopathy and its associated risk factors among adults in Ethiopia — a systematic review and meta-analysis.* Scientific Reports, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11569147/
7. *Diabetic retinopathy in Dakar and review of African literature: epidemiologic elements.* 2000. https://pubmed.ncbi.nlm.nih.gov/11011227/
10. *Epidemiology of diabetic retinopathy and maculopathy in Africa: a systematic review.* 2015. https://pmc.ncbi.nlm.nih.gov/articles/PMC4463765/
11. *APTOS 2019 Blindness Detection.* Kaggle. https://www.kaggle.com/competitions/aptos2019-blindness-detection
13. Gebru et al. *Datasheets for Datasets.* Communications of the ACM, 2021.

*(Reference numbers are shared across the DR-Africa family for cross-consistency.)*

---

## Citation

```bibtex
@misc{drafrica_grading_2026,
  title        = {DR-Grading: Fundus Diabetic-Retinopathy Grading with
                  Africa-Grounded Synthetic Clinical Context},
  author       = {DR-Africa dataset family},
  year         = {2026},
  note         = {Version 1.0.0. Images derived from APTOS 2019 (India);
                  clinical context is epidemiology-grounded synthetic data.},
  howpublished = {Hugging Face Datasets}
}
```