Evidence map›Paper›PMID 40883412›Full record

ArticleNPJ digital medicine2025

An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments.

Xinjian Chen, Jingtao Wang, Tianwei Qian, Jingcheng Wang, Yiming Ding, Su Zhang, Jingjing Liao, Qian Cheng, Ting Yang, Muhammad Mateen and 11 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

21 authors.

Xinjian Chen *School of Electronics and Information Engineering, Soochow University, Suzhou, China. xjchen@suda.edu.cn.
Jingtao Wang *School of Electronics and Information Engineering, Soochow University, Suzhou, China.
Tianwei QianDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
Jingcheng WangBigVison Medical Technology Ltd, Suzhou, Jiangsu, China.
Yiming DingSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Su ZhangSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Jingjing LiaoSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Qian ChengSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Ting YangSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Muhammad MateenSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Yu FanBigVison Medical Technology Ltd, Suzhou, Jiangsu, China.
Zongming SongHenan Eye Institute, Henan Provincial People's Hospital, Zhengzhou, China.
Jili ChenShibei Hospital Jingan District Shanghai, Shanghai, China.
Suyan LiDepartment of Ophthalmology, The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University, Xuzhou First People's Hospital, Xuzhou Eye Disease Prevention and Treatment Institute, Xuzhou, Jiangsu, China.
Jianmin HuFujian Medical University, Fuzhou, Fujian, China.
Wentao YanThe Eye Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Haoyu ChenJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Wencan WuFujian Medical University, Fuzhou, Fujian, China.
Jing HuangDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Tien Yin WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. wongtienyin@tsinghua.edu.cn.
Xun XuDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China. drxuxun@sjtu.edu.cn.

Funding

ational Key R&D Program of China 2018YFA0701700National Nature Science Foundation of China U20A20170Shanghai Medical Key Special Construction Project 2024ZDXK0054
6 · The paper itself

Abstract

Millions of individuals worldwide suffer from retinal anomalies, which can lead to irreversible vision loss. However, the number of ophthalmologists is highly mismatched with the population base in China, especially in many rural and underdeveloped towns. To tackle these challenges, this paper developed an Artificial Intelligence Cloud Platform for OCT-based Retinal Anomalies Screening (AI-PORAS), which is capable of detecting 15 retinal anomalies in OCT images to enhance remote diagnostic efficiency. AI-PORAS has been trained, validated, and deployed to 207 medical institutions in 29 provinces in China. The validation on 165,384 eyes with 3,551,959 OCT B-scan slices, AI-PORAS achieved an average accuracy of 93.16%, an AUC of 93.64%, a FPR of 6.82%, a FNR of 7.87%, matching the average performance level of attending ophthalmologists. Additionally, Statistical analysis of the 116,717 remotely diagnosed patients provided insightful guidance for healthcare decision-making and the development of tailored treatment plans.

Identifiers

PMID40883412
PMCPMC12397427

What OpenQuestion holds

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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.