Evidence map›Paper›PMID 40653573›Full record

ArticleNPJ digital medicine2025

Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning.

Yang Liu, Keming Zhao, Lihui Luo, Ziheng Zhang, Zhenghang Qian, Cenk Jiang, Zhicheng Du, Simin Deng, Chengming Yang, Duanpo Wu and 14 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 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

24 authors.

Yang Liu *Institute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Keming Zhao *Institute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Lihui LuoInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Ziheng ZhangInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Zhenghang QianInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Cenk JiangInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Zhicheng DuInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China.
Simin DengShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
Chengming YangSouthern University of Science and Technology Hospital, Shenzhen, China.
Duanpo WuHangzhou Dianzi University, Hangzhou, Zhejiang, China.
Shuai WangHangzhou Dianzi University, Hangzhou, Zhejiang, China.
Xingru HuangHangzhou Dianzi University, Hangzhou, Zhejiang, China.
Chenggang YanHangzhou Dianzi University, Hangzhou, Zhejiang, China.
Yingting ZhuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Yehong ZhuoState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Chunsheng QuClinical Laboratory of Lishui People's Hospital, First Affiliated Hospital of Lishui College, Wenzhou Medical College Lishui Hospital, Lishui, Zhejiang, China.
Jiaqi ChenClinical Laboratory of Lishui People's Hospital, First Affiliated Hospital of Lishui College, Wenzhou Medical College Lishui Hospital, Lishui, Zhejiang, China.
Zhenqiang HuangClinical Laboratory of Lishui People's Hospital, First Affiliated Hospital of Lishui College, Wenzhou Medical College Lishui Hospital, Lishui, Zhejiang, China.
Chenying LuZhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China.
Minjiang ChenZhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China.
Dongmei YuSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China.
Jiantao WangShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China. wangjiantao65@126.com.
Peiwu QinInstitute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China. pwqin@sz.tsinghua.edu.cn.
Jiansong JiZhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China. jjstcty@wmu.edu.cn.

Funding

Bureau of Planning, Land and Resources of Shenzhen Municipality (2022) 207National Natural Science Foundation of China 32350410397Science, Technology and Innovation Commission of Shenzhen Municipality JCYJ20240813112016022Shenzhen Medical Research Funds D2301002Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan JC2022009
6 · The paper itself

Abstract

Pathologic myopia is a leading cause of visual impairment and blindness. While deep learning-based approaches aid in recognizing pathologic myopia using color fundus photography, they often rely on implicit patterns that lack clinical interpretability. This study aims to diagnose pathologic myopia by identifying clinically significant morphologic patterns, specifically posterior staphyloma and myopic maculopathy, by leveraging ultra-widefield (UWF) images that provide a broad retinal field of view. We curate a large-scale, multi-source UWF myopia dataset called PSMM and introduce RealMNet, an end-to-end lightweight framework designed to identify these challenging patterns. Benefiting from the fast pretraining distillation backbone, RealMNet comprises only 21 million parameters, which facilitates deployment for medical devices. Extensive experiments conducted across three different protocols demonstrate the robustness and generalizability of RealMNet. RealMNet achieves an F1 Score of 0.7970 (95% CI 0.7612-0.8328), mAP of 0.8497 (95% CI 0.8058-0.8937), and AUROC of 0.9745 (95% CI 0.9690-0.9801), showcasing promise in clinical applications.

Identifiers

PMID40653573
PMCPMC12256625

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.