The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography
Related Experiment Video
Updated: May 7, 2026
Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
Published on: March 26, 2020
Lucas Szwarcberg1, Atif Anwer2, Alexandre Gozlan3
1Department of Ophthalmology, Dijon University Hospital, Université Bourgogne Europe, Dijon, France.
Introduction:
In recent years, retinal vascular imaging has attracted growing interest and is experiencing rapid technological advancements in imaging modalities such as swept-source optical coherence tomography angiography (SS OCT-A). OCT-A enables precise, noninvasive, quantitative measurements of retinal vascularization. However, it is also prone to artifacts that are challenging to detect and can significantly limit diagnostic accuracy and biomarker reliability.
Methods:
We present a dataset of en face retinal SS OCT-A images, labeled by artifact type and severity. Each patient's OCT-A scan consists of 14 images that are graded by multiple experts and assigned corresponding artifact labels with an overall quality grade. We also report results for two deep-learning binary classification models trained on the dataset: one based on image compression via principal component analysis (PCA) and the other using a six-channel tensor.
Results:
The dataset comprised 281 OCT-A scans from 115 anonymized patients. The PCA-based model achieved a precision of 87% for image quality classification, while the six-channel model achieved 97%.
Conclusion:
Using a curated dataset of en face SS OCT-A images with detailed artifact annotations, we trained a deep-learning model capable of high-precision image quality classification. These results demonstrate the feasibility of using task-specific artificial intelligence for quality assessment based on artifact detection in retinal imaging.
