ArticleBiomedical optics express2023
Longitudinal deep network for consistent OCT layer segmentation.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation.Biomedical optics express · 2025Article
- Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges.Advances in neural information processing systems · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.Biomedical optics express · 2024Article
- Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging.Biomedical optics express · 2024Article
- Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography.Frontiers in ophthalmology · 2024Article
- Deep learning network with differentiable dynamic programming for retina OCT surface segmentation.Biomedical optics express · 2023Article
- A machine learning framework for the quantification of experimental uveitis in murine OCT.Biomedical optics express · 2023Article
- Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network.IEEE access : practical innovations, open solutions · 2023Article
- Validation of Deep Learning-Based Automatic Retinal Layer Segmentation Algorithms for Age-Related Macular Degeneration with 2 Spectral-Domain OCT Devices.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.
Identifiers
What OpenQuestion holds
Registered trials
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.