ArticleNature communications2024
Physics-informed deep generative learning for quantitative assessment of the retina.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 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
11 citing papers in PubMed.
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- An algorithm for generating biophysically realistic three-dimensional arteriolar networks applied to rat skeletal muscle.Physiological reports · 2025Article
- Physics-Informed Neural Network-Based Pulsatile Flow Modeling and Targeted Drug Delivery Optimization in Computational Hemodynamics.Journal of pharmacy & bioallied sciences · 2025Article
- Mapping the arterial vascular network in an intact human kidney using hierarchical phase-contrast tomography.Npj imaging · 2025Article
- Forecasting the diabetic retinopathy progression using generative adversarial networks.Communications medicine · 2025Article
- Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Tube2FEM: a general-purpose highly automated pipeline for flow-related processes in (embedded) tubular objects.Royal Society open science · 2025Article
- UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.BMC medical imaging · 2025Article
- tUbeNet: a generalizable deep learning tool for 3D vessel segmentation.Biology methods & protocols · 2025Article
- Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina.Investigative ophthalmology & visual science · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.