Evidence map›Paper›PMID 41285945›Full record

ArticleScientific reports2025

Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical modeling.

Sojung Go, Jaemin Chae, Uichan Kim, Jongsoo Lim, Jooyoung Kim, Stephen Hogg, Emanuele Trucco, Sang Jun Park, Soochahn Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Sojung GoDepartment of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Jaemin ChaeDepartment of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Uichan KimDepartment of Electronics Engineering, Kookmin University, Seoul, Korea.
Jongsoo LimDepartment of Electronics Engineering, Kookmin University, Seoul, Korea.
Jooyoung KimXperix, Inc., Seoul, Korea.
Stephen HoggVAMPIRE project, Computing (SSEN), University of Dundee, Dundee, DD1 4HN, UK.
Emanuele TruccoVAMPIRE project, Computing (SSEN), University of Dundee, Dundee, DD1 4HN, UK.
Sang Jun ParkDepartment of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea. sangjunpark@snu.ac.kr.
Soochahn LeeDepartment of Electronics Engineering, Kookmin University, Seoul, Korea. sclee@kookmin.ac.kr.

Funding

Korea Environmental Industry and Technology Institute 2022003310001National Research Foundation of Korea NRF-2021R1A2C2095452
6 · The paper itself

Abstract

We present the SEoul Retinal Vessel Assessment Library (SERVAL), a novel software platform for precise quantitative measurement of vascular structures in fundus images. SERVAL integrates deep learning-based automatic artery and vein mask initialization, subpixel vessel centerline and boundary refinement, and interactive editing tools within a user-friendly graphical interface. From the refined artery and vein delineations, it enables accurate computation of a wide range of vessel assessment metrics, facilitating better characterization of complex vascular structures. We evaluate SERVAL through: (1) comparative analyses with existing platforms, highlighting its superior precision and structural detail; (2) longitudinal image studies demonstrating measurement consistency; and (3) a usability study confirming its clinical practicality. We expect SERVAL to serve as a valuable tool in clinical research, supporting the development of novel vascular biomarkers and diagnostic metrics for retinal and systemic diseases.

Indexed as

Deep LearningImage Processing, Computer-AssistedRetinal VesselsFundus OculiHumansRetinal DiseasesSoftwareDeep learningFundus imagesGraphic user interfaceRetinaVessel measurement

Identifiers

PMID41285945
PMCPMC12644878

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.