Evidence map›Paper›PMID 42189850›Full record

ArticlePloS one2026

Development and evaluation of AI model with deep learning for segmentation of extraocular muscles in thyroid eye disease.

Yusuke Haruna, Mizuki Tagami, Ryo Kurosaki, Mizuho Nishio, Gen Kinari, Mami Tomita, Norihiko Misawa, Yoshihiro Muragaki, Shigeru Honda

Abstract read
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In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Yusuke HarunaDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.
Mizuki TagamiDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.ORCID https://orcid.org/0000-0002-8771-7252
Ryo KurosakiDepartment of Medical Device Engineering, Graduate School of Medicine, Kobe University, Chuo-ku, Kobe-shi, Hyogo Prefecture, Japan.
Mizuho NishioDepartment of Medical Device Engineering, Graduate School of Medicine, Kobe University, Chuo-ku, Kobe-shi, Hyogo Prefecture, Japan.ORCID https://orcid.org/0000-0002-6037-2338
Gen KinariDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.
Mami TomitaDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.
Norihiko MisawaDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.
Yoshihiro MuragakiDepartment of Medical Device Engineering, Graduate School of Medicine, Kobe University, Chuo-ku, Kobe-shi, Hyogo Prefecture, Japan.
Shigeru HondaDepartment of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka-shi, Osaka-fu, Japan.ORCID https://orcid.org/0000-0002-0632-997X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and evaluate an AI model for the segmentation of extraocular muscles (EOMs) using Magnetic Resonance Imaging (MRI).

designSingle-center study with retrospective study.

methodsThe study included 52 patients with thyroid eye disease (TED) who underwent MRI examination of the orbital region at the Department of Ophthalmology, Osaka Metropolitan University, between October 2020 and June 2023. Manual labelling of all EOMs was performed on all slices. An AI model was created and compared with the manually labelled data using 5-fold cross validation on data sets of 12, 32, and 52 cases. The average signal intensity ratio (SIR) within the EOMs was measured. The primary outcome was the comparison of DICE similarity coefficients (DSC) as the agreement rate between AI's result and manual labelling amongst the three data sets (52 cases vs 32 cases vs 12 cases). The secondary outcome was the correlation of SIR between the AI and manual labelling.

resultsA significant difference in DSC was observed between the 12-, 32-, and 52-case data sets for the inferior rectus muscle and medial rectus muscle. A significant correlation was observed between the AI and manual labelling for SIR in all EOMs.

conclusionsAn AI model was successfully developed for the automatic segmentation of EOMs. There was little measurement error between AI and manual labelling, and the AI model using 52 cases had improved measurement accuracy compared to the AI models using 32 and 12 cases.

Indexed as

Deep LearningGraves OphthalmopathyOculomotor MusclesAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective Studies

Identifiers

PMID42189850
PMCPMC13210272

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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.