NCT05836246

Brief 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.

Trial Health

55
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
196

participants targeted

Target at P50-P75 for all trials

Timeline
Completed

Started Sep 2020

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
enrolling by invitation

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

September 1, 2020

Completed
2.6 years until next milestone

First Submitted

Initial submission to the registry

April 18, 2023

Completed
13 days until next milestone

First Posted

Study publicly available on registry

May 1, 2023

Completed
2.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 31, 2026

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

March 31, 2026

Completed
Last Updated

May 1, 2023

Status Verified

April 1, 2023

Enrollment Period

5.6 years

First QC Date

April 18, 2023

Last Update Submit

April 18, 2023

Conditions

Chronic Liver DiseaseThyroid DiseaseBenign Breast DiseaseMalignant Breast NeoplasmAcute Myocardial Infarction

Outcome Measures

Primary Outcomes (1)

  • Quantitative ultrasound information

    Quantitative ultrasound images of heart, thyroid, and breast disease

    5 years

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

A person who visits Seoul National University Bundang Hospital for medical treatment or consultation.

You may qualify if:

  • People with heart disease, thyroid disease, breast disease, and liver disease.

You may not qualify if:

  • Someone who has received surgery on the target organ in question.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Seoul National University Bundang Hospital

Seongnam-si, Gyeonggi-do, 13620, South Korea

Location

Related Publications (6)

  • 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.

    PMID: 27896451RESULT

  • 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.

    PMID: 28695342RESULT

  • 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.

    RESULT

  • 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.

    PMID: 28186630RESULT

  • 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.

    RESULT

  • 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.

    PMID: 27893402RESULT

MeSH Terms

Conditions

Thyroid DiseasesCystic FibrosisBreast Neoplasms

Condition Hierarchy (Ancestors)

Endocrine System DiseasesPancreatic DiseasesDigestive System DiseasesLung DiseasesRespiratory Tract DiseasesGenetic Diseases, InbornCongenital, Hereditary, and Neonatal Diseases and AbnormalitiesInfant, Newborn, DiseasesNeoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
PROSPECTIVE
Target Duration
1 Day
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

April 18, 2023

First Posted

May 1, 2023

Study Start

September 1, 2020

Primary Completion

March 31, 2026

Study Completion

March 31, 2026

Last Updated

May 1, 2023

Record last verified: 2023-04

Locations

KR(1)