Evidence map›Paper›PMID 40459922›Full record

ArticleOrbit (Amsterdam, Netherlands)2025

Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.

Sierra K Ha, Lisa Y Lin, Min Shi, Mengyu Wang, Ji Yun Han, Nahyoung Grace Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Orbit (Amsterdam, Netherlands), 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. Review
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

6 authors.

Sierra K HaOphthalmic Plastic Surgery Service, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Lisa Y LinOphthalmic Plastic Surgery Service, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Min ShiHarvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Mengyu WangHarvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.
Ji Yun HanThe Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Nahyoung Grace LeeOphthalmic Plastic Surgery Service, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, USA.

Funding

Personalizing Circumpapillary Retinal Nerve Fiber Layer Thickness Norms for GlaucomaR21EY035298 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI WANG, MENGYU · 2023 to 2023
$557k
NEI NIH HHS R21 EY035298
6 · The paper itself

Abstract

purposeTo develop a deep learning model using orbital computed tomography (CT) imaging to accurately distinguish thyroid eye disease (TED) and orbital myositis, two conditions with overlapping clinical presentations.

methodsRetrospective, single-center cohort study spanning 12 years including normal controls, TED, and orbital myositis patients with orbital imaging and examination by an oculoplastic surgeon. A deep learning model employing a Visual Geometry Group-16 network was trained on various binary combinations of TED, orbital myositis, and controls using single slices of coronal orbital CT images.

resultsA total of 1628 images from 192 patients (110 TED, 51 orbital myositis, 31 controls) were included. The primary model comparing orbital myositis and TED had accuracy of 98.4% and area under the receiver operating characteristic curve (AUC) of 0.999. In detecting orbital myositis, it had a sensitivity, specificity, and F1 score of 0.964, 0.994, and 0.984, respectively.

conclusionsDeep learning models can differentiate TED and orbital myositis based on a single, coronal orbital CT image with high accuracy. Their ability to distinguish these conditions based not only on extraocular muscle enlargement but also other salient features suggests potential applications in diagnostics and treatment beyond these conditions.

Indexed as

Deep LearningGraves OphthalmopathyOrbital MyositisTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveSensitivity and SpecificityArtificial intelligencecomputed tomographydeep learningorbital myositisthyroid eye disease

Identifiers

PMID40459922
PMCPMC12265679

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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.