Evidence map›Paper›PMID 41115948›Full record

ArticleNPJ digital medicine2025

A deep learning based automatic report generator for retinal optical coherence tomography images.

Xinjian Chen, Huazhu Fu, Jingtao Wang, Tian Lin, Qian Cheng, Cangxin Li, Meng Wang, Zhongyue Chen, Aidi Lin, Anlin Zhang and 13 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 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

23 authors.

Xinjian Chen *Health Management Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Huazhu Fu *Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore.
Jingtao Wang *School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Tian Lin *Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Qian ChengSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Cangxin LiSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Meng WangSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Zhongyue ChenSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Aidi LinJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Anlin ZhangJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Weifang ZhuSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Shirong ChenJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Fei ShiSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Dehui XiangSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Baoqing NieSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Yi ZhouSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Yuanyuan PengSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Danqi FangDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.
Chao GuoSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Ting WangSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Mingzhi ZhangJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Chi Pui PangJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
Haoyu ChenJoint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China. drchenhaoyu@gmail.com.

Funding

Agency for Science, Technology and Research (A*STAR) Career Development Fund C222812010Department of Education of Guangdong Province 2024ZDZX2024National Natural Science Foundation of China 62271337National Natural Science Foundation of China 62371328National Natural Science Foundation of China U20A20170National Nature Science Foundation of China 62371326Natural Science Foundation of Jiangsu Province of China BK20211308Shantou Science and Technology Program 190917085269835the National Key R&D Program of China 2018YFA0701700
6 · The paper itself

Abstract

Reading and summarizing insights from Optical Coherence Tomography (OCT) images is a routine yet time-consuming task that requires expensive time from experienced ophthalmologists. This paper introduces the Multi-label OCT Report Generation (MORG) model, a deep learning approach to assist in the interpretation of OCT images. MORG employs dual image encoders to extract features from OCT image pairs, fusing them through a multi-scale module with an attention mechanism, followed by a sentence decoder to produce reports. Trained and tested on 57,308 retinal OCT image pairs, MORG achieved high classification accuracy for 16 pathologies with 37 descriptive types. It also excelled in a blind grading test against general large language models and other state-of-the-art image captioning models, scoring 4.55 compared to ophthalmologists' 4.63 out of a maximum of 5. Furthermore, MORG has the potential to reduce the report drafting time for ophthalmologists by 58.9%, significantly alleviating their workload.

Identifiers

PMID41115948
PMCPMC12537946

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.