Evidence map›Paper›PMID 42414734›Full record

ArticleInternational journal of colorectal disease2026

Scleral image analysis via artificial intelligence to evaluate inflammatory status in inflammatory bowel disease.

Ying Li, Pinyi Huang, Lei Wang, Shuijiao Chen, Guanghui Lian, Fujun Li, Xiaowei Liu, Fanggen Lu, Yani Yin

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Article in International journal of colorectal disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Ying LiDepartment of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, China.
Pinyi HuangCollege of Electronic Science and Technology, NUDT, Changsha, China.
Lei WangSchool of Computer Science and Engineering, Central South University, Changsha, China.
Shuijiao ChenDepartment of Gastroenterology of Xiangya Hospital, Central South University, Changsha, China.
Guanghui LianDepartment of Gastroenterology of Xiangya Hospital, Central South University, Changsha, China.
Fujun LiDepartment of Gastroenterology of Xiangya Hospital, Central South University, Changsha, China.
Xiaowei LiuDepartment of Gastroenterology of Xiangya Hospital, Central South University, Changsha, China.
Fanggen LuDepartment of Gastroenterology, the Second Xiangya Hospital of Central South University, Changsha, China.
Yani YinDepartment of Gastroenterology of Xiangya Hospital, Central South University, Changsha, China. yinyani@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAmong the extraintestinal ocular manifestations of patients with inflammatory bowel disease (IBD), episcleritis is associated with the inflammatory activity of IBD. The use of artificial intelligence to evaluate scleral images may help establish a non-invasive method for assessing the degree of inflammation. This study aims to provide a new approach for evaluating IBD inflammation by analyzing scleral images.

methodsWe collected scleral images from patients with IBD across different inflammatory states and from controls. Then, we analyzed the images using the MASK R-CNN and HRNet method to build models for assessing the degree of inflammation.

resultsA total of 942 scleral images were collected and divided into 5 groups: the control group, inflammatory Crohn's disease (ICD) group, CD in remission (RCD) group, inflammatory ulcerative colitis (IUC) group, and UC in remission (RUC) group. In the training set, the accuracy and F1-score for discriminating between IBD and non-IBD controls, inflammatory IBD and IBD in remission, ICD and RCD, IUC and RUC were 99.02%, 97.99%, and 98.44%, 98.34%, and 99.04%, 98.98%, and 100%, 100%, respectively. In the test set, the accuracy, F1-score and the Area Under the Curve (AUC) were 95.59%, 92.5%, 92.9%, and 85.1%, 81.2%, 85.8%, and 74.2%, 72.5%, 77.9%, and 92.9%, 91.1%, 91.3%, respectively. The inflammatory ulcerative colitis group exhibited the most prominent scleral congestion, which was correlated with disease severity.

conclusionIn this small-sample exploratory study, we propose that the evaluation of ocular manifestations in patients with IBD may serve as a non-invasive method for assessing inflammatory activity, especially in patients with UC.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedInflammationInflammatory Bowel DiseasesScleraAdultColitis, UlcerativeFemaleHumansMaleMiddle AgedROC CurveScleritisArtificial intelligenceInflammationInflammatory bowel diseasesScleral image

Identifiers

PMID42414734
PMCPMC13627171

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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.