The Development of Quantitative Ultrasound Imaging Software Platform
NCT ID: NCT05836246
Last Updated: 2023-05-01
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
196 participants
OBSERVATIONAL
2020-09-01
2026-03-31
Brief Summary
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The main question it aims to answer is to evaluate the effectiveness by determining that the new image analysis method is considered valid if it helps to identify more than 30% of histological characteristics.
Participants will undergo the examination using the two methods mentioned earlier after signing the consent form.
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Detailed Description
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Conditions
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Study Design
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OTHER
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Seoul National University Bundang Hospital
OTHER
Responsible Party
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Locations
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Seoul National University Bundang Hospital
Seongnam-si, Gyeonggi-do, South Korea
Countries
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References
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Cheng PM, Malhi HS. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images. J Digit Imaging. 2017 Apr;30(2):234-243. doi: 10.1007/s10278-016-9929-2.
Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network. J Digit Imaging. 2017 Aug;30(4):477-486. doi: 10.1007/s10278-017-9997-y.
F. Milletari, N. Navab and S. -A. Ahmadi. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA. 2016; 565-571.
Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys. 2017 May;44(5):1678-1691. doi: 10.1002/mp.12134. Epub 2017 Apr 17.
Chen H, Zheng Y, Park JH, Heng PA, Zhou SK. (2016). Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 2016; 9901.
Lekadir K, Galimzianova A, Betriu A, Del Mar Vila M, Igual L, Rubin DL, Fernandez E, Radeva P, Napel S. A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE J Biomed Health Inform. 2017 Jan;21(1):48-55. doi: 10.1109/JBHI.2016.2631401. Epub 2016 Nov 22.
Other Identifiers
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B-1910-570-301
Identifier Type: -
Identifier Source: org_study_id
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