Evidence map›Paper›PMID 40778358›Full record

ArticleOphthalmology science

Automated Segmentation of Subretinal Fluid from OCT: A Vision Transformer Approach with Cross-Validation.

Julie Midroni, Jack Longwell, Nishaant Bhambra, Sueellen Demian, Aurora Pecaku, Isabela Martins Melo, Rajeev H Muni

Abstract read
In one paragraph

Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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

7 authors.

Julie MidroniDepartment of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Jack LongwellTemerty Centre for Artificial Intelligence Research and Education in Medicine, Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Nishaant BhambraDepartment of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Sueellen DemianDepartment of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada.
Aurora PecakuDepartment of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada.
Isabela Martins MeloDepartment of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada.
Rajeev H MuniDepartment of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: We present an algorithm to segment subretinal fluid (SRF) on individual B-scan slices in patients with rhegmatogenous retinal detachment (RRD). Particular attention is paid to robustness, with a fivefold cross-validation approach and a hold-out test set. Design: Retrospective, cross-sectional study. Participants: A total of 3819 B-scan slices across 98 time points from 45 patients were used in this study. Methods: Subretinal fluid was segmented on all scans. A base SegFormer model, pretrained on 4 massive data sets, was further trained on raw B-scans from the retinal OCT fluid challenge data set of 4532 slices: an open data set of intraretinal fluid, SRF, and pigment epithelium detachment. When adequate performance was reached, transfer learning was used to train the model on our in-house data set, to segment SRF by generating a pixel-wise mask of presence/absence of SRF. A fivefold cross-validation approach was used, with an additional hold-out test set. All folds were first trained and cross-validated and then additionally tested on the hold-out set. Mean (averaged across images) and total (summed across all pixels, irrespective of image) Dice coefficients were calculated for each fold. Main Outcome Measures: Subretinal fluid volume after surgical intervention for RRD. Results: The average total Dice coefficient across the validation folds was 0.92, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. For the test set, the average total Dice coefficient was 0.94, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. The model showed strong interfold consistency on the hold-out set, with a standard deviation of only 0.03. Conclusions: The SegFormer model for SRF segmentation demonstrates a strong ability to segment SRF. This result holds up to cross-validation and hold-out testing, across all folds. The model is available open-source online. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Deep learningMachine learningRetinal detachmentSegmentationSubretinal fluid

Identifiers

PMID40778358
PMCPMC12329092

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LicenceCC BY-NC-ND
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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.