Evidence map›Paper›PMID 39046593›Full record

ArticleEndocrine2024

CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

Zijia Liu, Kexin Tan, Haiyang Zhang, Jing Sun, Yinwei Li, Sijie Fang, Jipeng Li, Xuefei Song, Huifang Zhou, Guangtao Zhai

Abstract read
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In one paragraph

Article in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

10 authors.

Zijia Liu *Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China.
Kexin Tan *Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Haiyang ZhangDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Jing SunDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Yinwei LiDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Sijie FangDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Jipeng LiDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Xuefei SongDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. songxuefei@shsmu.edu.cn.ORCID 0000-0003-4421-6017
Huifang ZhouDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, and Center for Basic Medical Research and Innovation in Visual System Diseases of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. fangzzfang@163.com.ORCID 0000-0002-8636-861X
Guangtao ZhaiInstitute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China. zhaiguangtao@sjtu.edu.cn.

Funding

Clinical Acceleration Program of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine JYLJ202202Clinical Research Plan of SHDC SHDC2020CR3051BNational Natural Science Foundation of China 81930024Science and Technology Commission of Shanghai 20DZ22708Shanghai Key Clinical Specialty, Shanghai Eye Disease Research Center 2022ZZ01003
6 · The paper itself

Abstract

purposeThyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, while its evaluation is quite difficult. The present study aimed to introduce an artificial intelligence (AI) model based on orbital computed tomography (CT) images for ocular motility score.

methodsA total of 410 sets of CT images and clinical data were obtained from the hospital. To build a triple classification predictive model for ocular motility score, multiple deep learning models were employed to extract features of images and clinical data. Subgroup analyses based on pertinent clinical features were performed to test the efficacy of models.

resultsThe ResNet-34 network outperformed Alex-Net and VGG16-Net in prediction of ocular motility score, with the optimal accuracy (ACC) of 0.907, 0.870, and 0.890, respectively. Subgroup analyses indicated no significant difference in ACC between active or inactive phase, functional visual field diplopia or peripheral visual field diplopia (p > 0.05). However, in the gender subgroup, the prediction model performed more accurately in female patients than males (p = 0.02).

conclusionIn conclusion, the AI model based on CT images and clinical data successfully realized automatic scoring of ocular motility in TED patients. This approach potentially enhanced the efficiency and accuracy of ocular motility evaluation, thus facilitating clinical application.

Indexed as

Artificial IntelligenceEye MovementsGraves OphthalmopathyAdultEyeFemaleHumansMaleMiddle AgedNeural Networks, ComputerTomography, X-Ray ComputedArtificial intelligenceComputed tomographyDeep learningOcular motilityThyroid eye disease

Identifiers

PMID39046593

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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.