The Development of Quantitative Ultrasound Imaging Software Platform

Acute Myocardial Infarction Clinical Trial

Official title:

The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05836246
• Other study ID #: B-1910-570-301
• Status: Enrolling by invitation
• 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
• 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.

Related Conditions & MeSH terms

• Acute Myocardial Infarction
• Benign Breast Disease
• Breast Diseases
• Breast Neoplasms
• Chronic Liver Disease
• Infarction
• Liver Diseases
• Malignant Breast Neoplasm
• Myocardial Infarction
• Thyroid Disease
• Thyroid Diseases

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