ArticleOphthalmic research2026
The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.
Article in Ophthalmic research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.Ophthalmic research · 2026Article
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8 authors.
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Abstract
introductionIn 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.
methodsWe 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.
resultsThe 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%.
conclusionUsing 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.
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