Evidence map›Paper›PMID 38686594›Full record

ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2024

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

Ameen Amanian, Aseem Jain, Yuliang Xiao, Chanha Kim, Andy S Ding, Manish Sahu, Russell Taylor, Mathias Unberath, Bryan K Ward, Deepa Galaiya and 2 more

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Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2024. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

12 authors.

Ameen AmanianDepartment of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-4418-8215
Aseem JainDepartment of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0003-2105-4940
Yuliang XiaoDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Chanha KimDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Andy S DingDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Manish SahuDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Russell TaylorDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Mathias UnberathDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Bryan K WardDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Deepa GalaiyaDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Masaru IshiiDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Francis X CreightonDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Funding

Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base SurgeryK08DC019708 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI CREIGHTON, FRANCIS XAVIER · 2021 to 2025
$959k
NIDCD NIH HHS K08 DC019708
6 · The paper itself

Abstract

objectiveObtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures. STUDY

designRetrospective cohort.

settingTertiary referral center.

methodsFrom a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU-Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU-Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC).

resultsThe AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7-5.3 mm) and was located on average 1.9 mm caudal from the bony cartilaginous junction.

conclusionThis study describes the first end-to-end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large-scale studies exploring the relationship between ET pathologies and the 3D shape of the ET.

Indexed as

Carotid Artery, InternalDeep LearningEustachian TubeImaging, Three-DimensionalTomography, X-Ray ComputedAdultFemaleHumansMaleMiddle AgedRetrospective Studiesartificial intelligencedeep learningEustachian tubeEustachian tube balloon dilationEustachian tube dysfunctionmedical imaging

Identifiers

PMID38686594
PMCPMC11349465

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.