Evidence map›Paper›PMID 40410547›Full record

ArticleNPJ digital medicine2025

Advanced and interpretable corneal staining assessment through fine grained knowledge distillation.

Yuqing Deng, Pujin Cheng, Ruiwen Xu, Lirong Ling, Hongliang Xue, Shiyou Zhou, Yansong Huang, Junyan Lyu, Zhonghua Wang, Kenneth K Y Wong and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

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

2 citing papers in PubMed.

  1. Article
  2. Review
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

18 authors.

Yuqing Deng *State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Pujin Cheng *Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
Ruiwen XuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Lirong LingState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Hongliang XueThe Key Laboratory of Advanced Interdisciplinary Studies, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shiyou ZhouState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Yansong HuangDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
Junyan LyuDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
Zhonghua WangDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
Kenneth K Y WongDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China.
Yimin ZhangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Kang YuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Tingting ZhangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Xiaoqing HuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Xiaoyi LiZhaoke (Guangzhou) Ophthalmology Pharmaceutical Ltd, Guangzhou, China.
Xiaoying TangDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China. tangxy@sustech.edu.cn.
Yan LouDepartment of Computer, School of Intelligent Medicine, China Medical University, Shenyang, China. ylou04@cmu.edu.cn.
Jin YuanBeijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing, China. yuanjincornea@126.com.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82230033
6 · The paper itself

Abstract

The assessment of corneal fluorescein staining is essential, yet current AI models for Corneal Staining Score (CSS) assessments inadequately identify punctate lesions due to annotation challenges and noise, risk misrepresenting treatment responses through "plateau" effects, and highlight the necessity for real-world evaluations to enhance disease severity assessments. To address these limitations, we developed the Fine-grained Knowledge Distillation Corneal Staining Score (FKD-CSS) model. FKD-CSS integrates fine-grained features into CSS grading, providing continuous and nuanced scores with interpretability. Trained on corneal staining images collected from dry eye (DE) patients across 14 hospitals, FKD-CSS achieved robust accuracy, with a Pearson's r of 0.898 and an AUC of 0.881 in internal validation, matching senior ophthalmologists' performance. External tests on 2376 images from 23 hospitals across China further validated its efficacy (r: 0.844-0.899, AUC: 0.804-0.883). Additionally, FKD-CSS demonstrated generalizability in multi-ocular-surface-disease testing, underscoring its potential in handling different staining patterns.

Identifiers

PMID40410547
PMCPMC12102350

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Registered trials

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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.