Evidence map›Paper›PMID 41152744›Full record

ArticleBMC medical imaging2025

Automated cup-to-disc ratio quantification via color fundus photography for chronic glaucoma screening.

Xiaoxuan Lv, Yang Yang, Cheng Wan, Jiani Zhao, Wei Chi, Weihua Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

6 authors.

Xiaoxuan LvCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Yang YangCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Cheng WanCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Jiani ZhaoCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
Wei ChiShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University, Shenzhen, 518040, China. chiwei@mail.sysu.edu.cn.
Weihua YangShenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University, Shenzhen, 518040, China. benben0606@139.com.

Funding

National Natural Science Foundation of China 82571272Sanming Project of Medicine in Shenzhen No. SZSM202411007the Key Special Project of 'Cutting-Edge Biotechnology' in the National Key Research and Development Program of China 2024YFC3406200
6 · The paper itself

Abstract

purposeGlaucoma is a leading cause of irreversible blindness, and accurate cup-to-disc ratio (CDR) measurement is essential for early detection. This study presents an enhanced deep learning–based system for automated CDR estimation and glaucoma screening.

methodsWe propose an end-to-end framework consisting of three modules: (1) optic cup and disc segmentation using an enhanced dual encoder–decoder network (E-DCoAtUNet), (2) a conditional random field (CRF) post-processing module for boundary refinement, and (3) a measurement module for vertical CDR calculation and glaucoma classification. The model was trained and evaluated on the Drishti-GS dataset and validated on the REFUGE dataset to assess generalizability.

resultsThe system achieved Dice scores of 97.6% for the optic disc and 90.8% for the optic cup, further improved by CRF refinement. Automated CDR estimation showed strong agreement with expert annotations (Pearson’s r = 0.9190, MAE = 0.0387). For glaucoma screening, the system demonstrated reliable performance across both datasets, highlighting its robustness and clinical applicability.

conclusionThe proposed E-DCoAtUNet-based system provides a fully automated, interpretable, and precise solution for glaucoma screening. By integrating advanced segmentation, boundary refinement, and accurate measurement, it ensures consistent CDR evaluation even under challenging imaging conditions, and demonstrates strong potential for real-world clinical application.

Indexed as

GlaucomaOptic DiskPhotographyChronic DiseaseDeep LearningFundus OculiHumansImage Interpretation, Computer-AssistedAuxiliary diagnosisColor fundus photographyCup-to-disc ratioDeep learningGlaucomaImage segmentation

Identifiers

PMID41152744
PMCPMC12560540

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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.