AROMA Retinal OCTA Artifact Dataset

Retinal OCTA scans labeled for artifact type, artifact severity, signal strength, and image quality.

At a glance

Field	Value
Short name	aroma_octa
Full name	AROMA Retinal OCTA Artifact Dataset
Primary category	octa
Contained modalities	octa
Tasks	quality_assessment, classification, grading
Samples	281
Classes	Not reported (Not reported)
Splits	all
Size	1.011 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
Descriptive screening label	Standard label without an explicit NC clause; not a permission finding
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Zenodo
Availability	available (checked 2026-07-21)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Metadata and access only

Notes

The source reports 281 scans from 115 patients. Fourteen en-face images per scan yield 3,934 derived images with seven artifact types graded on a four-level severity scale.

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download aroma_octa --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download aroma_octa --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('aroma_octa')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: https://doi.org/10.5281/zenodo.18258095

Source-term evidence: https://doi.org/10.5281/zenodo.18258095

Loader status

This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.

Citation

BibTeX

@misc{aroma_octa,
  title  = { AROMA Retinal OCTA Artifact Dataset },
  note   = { Szwarcberg L, Anwer A, Gozlan A, et al. The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography. Ophthalmic Research. 2026. doi:10.1159/000551126 },
  year   = { 2026 },
  url    = { https://doi.org/10.5281/zenodo.18258095 },
}

Plain text

Szwarcberg L, Anwer A, Gozlan A, et al. The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography. Ophthalmic Research. 2026. doi:10.1159/000551126

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• Apparent scope: dataset_files
• Descriptive screening label: Standard label without an explicit NC clause; not a permission finding

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

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