Ophthalmic research · 2026
The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Szwarcberg Lucas, Anwer Atif, Gozlan Alexandre, Drouard Paul, Gabrielle Pierre-Henry, Creuzot-Garcher Catherine, Mériaudeau Fabrice, Arnould Louis
Аннотация
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.
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.