The Development of Quantitative Ultrasound Imaging Softwa... | NCT05836246

Studies
The Development of Quantitative Ultrasound Imaging Software Platform

NCT05836246

Sponsor

Seoul National University Bundang Hospital

Status

Unknown status

Last Update Posted

May 1, 2023Actual

Enrollment

196Estimated

Phase

Not provided

Conditions

Chronic Liver Disease
Thyroid Disease
Benign Breast Disease
Malignant Breast Neoplasm
Acute Myocardial Infarction

Interventions

Not provided

Countries

South Korea

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Apr 2023

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Enrolling by invitation

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Sep 1, 2020Actual

Primary Completion Date

Mar 31, 2026Estimated

Completion Date

Mar 31, 2026Estimated

First Submitted Date

Apr 18, 2023

First Submission Date that Met QC Criteria

Apr 18, 2023

First Posted Date

May 1, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Apr 18, 2023

Last Update Posted Date

May 1, 2023Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

1 Day

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

196Estimated

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Quantitative ultrasound information	Quantitative ultrasound images of heart, thyroid, and breast disease	5 years

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Seoul National University Bundang Hospital		Seongnam-si	Gyeonggi-do	13620	South Korea

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
27896451	Result	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.
28695342	Result	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.

ID	Term
D013959	Thyroid Diseases
D003550	Cystic Fibrosis
D001943	Breast Neoplasms

ID	Term
D004700	Endocrine System Diseases
D010182	Pancreatic Diseases
D004066	Digestive System Diseases
D008171	Lung Diseases