The Development of Quantitative Ultrasound Imaging Software Platform
April 18, 2023 updated by: Seoul National University Bundang Hospital
The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
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.
Study Overview
Status
Enrolling by invitation
Study Type
Observational
Enrollment (Anticipated)
196
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
Gyeonggi-do
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Seongnam, Gyeonggi-do, Korea, Republic of, 13620
- Seoul National University Bundang Hospital
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Sampling Method
Non-Probability Sample
Study Population
A person who visits Seoul National University Bundang Hospital for medical treatment or consultation.
Description
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 Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Quantitative ultrasound information
Time Frame: 5 years
|
Quantitative ultrasound images of heart, thyroid, and breast disease
|
5 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
September 1, 2020
Primary Completion (Anticipated)
March 31, 2026
Study Completion (Anticipated)
March 31, 2026
Study Registration Dates
First Submitted
April 18, 2023
First Submitted That Met QC Criteria
April 18, 2023
First Posted (Actual)
May 1, 2023
Study Record Updates
Last Update Posted (Actual)
May 1, 2023
Last Update Submitted That Met QC Criteria
April 18, 2023
Last Verified
April 1, 2023
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Digestive System Diseases
- Ischemia
- Pathologic Processes
- Necrosis
- Myocardial Ischemia
- Heart Diseases
- Cardiovascular Diseases
- Vascular Diseases
- Skin Diseases
- Neoplasms
- Neoplasms by Site
- Endocrine System Diseases
- Liver Diseases
- Myocardial Infarction
- Infarction
- Breast Neoplasms
- Thyroid Diseases
- Breast Diseases
- Fibrocystic Breast Disease
Other Study ID Numbers
- B-1910-570-301
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.