Evidence map›Paper›PMID 37822004›Full record

ArticleInternational journal of retina and vitreous2023

Pseudoaveraging for denoising of OCT angiography: a deep learning approach for image quality enhancement in healthy and diabetic eyes.

Omar Abu-Qamar, Warren Lewis, Luisa S M Mendonca, Luis De Sisternes, Adam Chin, A Yasin Alibhai, Isaac Gendelman, Elias Reichel, Stephanie Magazzeni, Sophie Kubach and 5 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of retina and vitreous, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Widefield OCT angiography.Progress in retinal and eye research · 2025
    Review
  5. Advances in OCT Angiography.Translational vision science & technology · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Omar Abu-QamarNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Warren LewisResearch and Development, Carl Zeiss Meditec, Dublin, CA, 94568, USA.
Luisa S M MendoncaNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Luis De SisternesResearch and Development, Carl Zeiss Meditec, Dublin, CA, 94568, USA.
Adam ChinNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
A Yasin AlibhaiBoston Image Reading Center, 55 Causeway street, Boston, MA, 02114, USA.
Isaac GendelmanNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Elias ReichelNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Stephanie MagazzeniResearch and Development, Carl Zeiss Meditec, Dublin, CA, 94568, USA.
Sophie KubachResearch and Development, Carl Zeiss Meditec, Dublin, CA, 94568, USA.
Mary DurbinResearch and Development, Carl Zeiss Meditec, Dublin, CA, 94568, USA.
Andre J WitkinNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Caroline R BaumalNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Jay S DukerNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA.
Nadia K WaheedNew England Eye Center, Tufts Medical Center, 800 Washington St., Box 450, Boston, MA, 02111, USA. nadiakwaheed@gmail.com.ORCID http://orcid.org/0000-0002-8229-7519

Funding

Carl Zeiss Meditec AG Device Loan
6 · The paper itself

Abstract

backgroundThis study aimed to develop a deep learning (DL) algorithm that enhances the quality of a single-frame enface OCTA scan to make it comparable to 4-frame averaged scan without the need for the repeated acquisitions required for averaging.

methodsEach of the healthy eyes and eyes from diabetic subjects that were prospectively enrolled in this cross-sectional study underwent four repeated 6 × 6 mm macular scans (PLEX Elite 9000 SS-OCT), and the repeated scans of each eye were co-registered to produce 4-frame averages. This prospective dataset of original (single-frame) enface scans and their corresponding averaged scans was divided into a training dataset and a validation dataset. In the training dataset, a DL algorithm (named pseudoaveraging) was trained using original scans as input and 4-frame averages as target. In the validation dataset, the pseudoaveraging algorithm was applied to single-frame scans to produce pseudoaveraged scans, and the single-frame and its corresponding averaged and pseudoaveraged scans were all qualitatively compared. In a separate retrospectively collected dataset of single-frame scans from eyes of diabetic subjects, the DL algorithm was applied, and the produced pseudoaveraged scan was qualitatively compared against its corresponding original.

resultsThis study included 39 eyes that comprised the prospective dataset (split into 5 eyes for training and 34 eyes for validating the DL algorithm), and 105 eyes that comprised the retrospective test dataset. Of the total 144 study eyes, 58% had any level of diabetic retinopathy (with and without diabetic macular edema), and the rest were from healthy eyes or eyes of diabetic subjects but without diabetic retinopathy and without macular edema. Grading results in the validation dataset showed that the pseudoaveraged enface scan ranked best in overall scan quality, background noise reduction, and visibility of microaneurysms (p < 0.05). Averaged scan ranked best for motion artifact reduction (p < 0.05). Grading results in the test dataset showed that pseudoaveraging resulted in enhanced small vessels, reduction of background noise, and motion artifact in 100%, 82%, and 98% of scans, respectively. Rates of false-positive/-negative perfusion were zero.

conclusionPseudoaveraging is a feasible DL approach to more efficiently improve enface OCTA scan quality without introducing notable image artifacts.

Indexed as

AveragingDeep learningDenoisingDiabetic retinopathyImage artifactImage qualityOCTAOptical coherence tomography angiographyPseudoaveraging

Identifiers

PMID37822004
PMCPMC10568842

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.