Evidence map›Paper›PMID 39706192›Full record

ArticleCell reports. Medicine2025

Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.

Yuanyuan Peng, Aidi Lin, Meng Wang, Tian Lin, Linna Liu, Jianhua Wu, Ke Zou, Tingkun Shi, Lixia Feng, Zhen Liang and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
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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.

Yuanyuan PengSchool of Biomedical Engineering, Anhui Medical University, Hefei, Anhui 230032, China.
Aidi LinJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong 515041, China.
Meng WangCentre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore.
Tian LinJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong 515041, China.
Linna LiuWuhan Aier Eye Hospital, Wuhan, Hubei 430063, China.
Jianhua WuWuhan Aier Eye Hospital, Wuhan, Hubei 430063, China.
Ke ZouNational Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China.
Tingkun ShiJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong 515041, China.
Lixia FengDepartment of Ophthalmology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Zhen LiangSchool of Biomedical Engineering, Anhui Medical University, Hefei, Anhui 230032, China; The Affiliated Chuzhou Hospital of Anhui Medical University, First People's Hospital of Chuzhou, Chuzhou, Anhui 239099, China.
Tao LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Dan LiangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Shanshan YuState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong 510000, China.
Dawei SunDepartment of Ophthalmology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China.
Jing LuoDepartment of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
Ling GaoDepartment of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
Xinjian ChenSchool of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu 215006, China; State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, Jiangsu 215006, China.
OCT reading groupJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong 515041, China.
Ching-Yu ChengCentre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore, Singapore.
Huazhu FuInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore. Electronic address: hzfu@ieee.org.
Haoyu ChenJoint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong 515041, China. Electronic address: drchenhaoyu@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieves a higher F1 score of 95.74% than other state-of-the-art algorithms (92.03%-93.66%) and improves to 97.44% with threshold strategy. The model achieves similar excellent performance on two external test sets from the same and different OCT machines. In human-model comparison, FMUE achieves a higher F1 score of 96.30% than retinal experts (86.95%, p = 0.004), senior doctors (82.71%, p < 0.001), junior doctors (66.55%, p < 0.001), and generative pretrained transformer 4 with vision (GPT-4V) (32.39%, p < 0.001). Besides, FMUE predicts high uncertainty scores for >85% images of non-target-category diseases or with low quality to prompt manual checks and prevent misdiagnosis. Our FMUE provides a trustworthy method for automatic retinal anomaly detection in a clinical open-set environment.

Indexed as

Artificial IntelligenceRetinal DiseasesTomography, Optical CoherenceAlgorithmsHumansReproducibility of ResultsRetinaUncertaintyfoundation modeloptical coherence tomographyout-of-distribution detectionretinal diseaseuncertainty estimation

Identifiers

PMID39706192
PMCPMC11866418

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.