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Clinical Trial Details — Status: Enrolling by invitation

Administrative data

NCT number NCT05836246
Other study ID # B-1910-570-301
Secondary ID
Status Enrolling by invitation
Phase
First received
Last updated
Start date September 1, 2020
Est. completion date March 31, 2026

Study information

Verified date April 2023
Source Seoul National University Bundang Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational [Patient Registry]

Clinical Trial Summary

The goal of this observational study is to compare the image differences between conventional ultrasound and artificial intelligence-based ultrasound software in conscious adults. 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.


Recruitment information / eligibility

Status Enrolling by invitation
Enrollment 196
Est. completion date March 31, 2026
Est. primary completion date March 31, 2026
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - People with heart disease, thyroid disease, breast disease, and liver disease. Exclusion Criteria: - Someone who has received surgery on the target organ in question.

Study Design


Locations

Country Name City State
Korea, Republic of Seoul National University Bundang Hospital Seongnam Gyeonggi-do

Sponsors (1)

Lead Sponsor Collaborator
Seoul National University Bundang Hospital

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (6)

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

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. — View Citation

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. — View Citation

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.

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): — View Citation

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. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Quantitative ultrasound information Quantitative ultrasound images of heart, thyroid, and breast disease 5 years
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