Chronic Liver DiseaseNCT05836246
The Development of Quantitative Ultrasound Imaging Software Platform
An observational study of Chronic Liver Disease and Thyroid Disease, run by Seoul National University Bundang Hospital at 1 site in South Korea.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chronic Liver Disease, Thyroid Disease, Benign Breast Disease
- How many people
- 196 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Sep 2020to 31 Mar 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: A person who visits Seoul National University Bundang Hospital for medical treatment or consultation.
Who may be able to join
- People with heart disease, thyroid disease, breast disease, and liver disease.
Who cannot join
- Someone who has received surgery on the target organ in question.
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Seoul National University Bundang Hospital
Seongnam-si, Gyeonggi-do, 13620
About this study
Official title: 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.
- Conditions
- Chronic Liver Disease, Thyroid Disease, Benign Breast Disease, Malignant Breast Neoplasm, Acute Myocardial Infarction
- Medical subject headings
- Thyroid Diseases, Cystic Fibrosis, Breast Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Quantitative ultrasound information
Quantitative ultrasound images of heart, thyroid, and breast disease
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2023
- Study start1 Sep 2020
- Primary completion31 Mar 2026 (estimated)
- Study completion31 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Chronic Liver DiseaseStudies of the same condition, open and under way.
- More trials run by Seoul National University Bundang HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 196 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Seoul National University Bundang Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05836246
- Sponsor's protocol ID
- B-1910-570-301
- Status last verified
- Apr 2023
- First submitted
- 18 Apr 2023
- Last updated
- 1 May 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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.
PubMed 27896451Chi 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.
PubMed 28695342F. 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.
PubMed 28186630Chen 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.
PubMed 27893402
Study at a glance
From the registry record, last updated by the sponsor on 1 May 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
- ClinicalTrials.gov ID
- NCT05836246
- Acronym / sponsor ID
- B-1910-570-301
- Conditions
- Chronic Liver Disease, Thyroid Disease, Benign Breast Disease, Malignant Breast Neoplasm, Acute Myocardial Infarction
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 196 participants (estimated)
- Lead sponsor
- Seoul National University Bundang HospitalUniversity, hospital or other
- Study start
- 1 Sep 2020 (actual)
- Primary completion
- 31 Mar 2026 (estimated)
- Study completion
- 31 Mar 2026 (estimated)
- First posted
- 1 May 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- A person who visits Seoul National University Bundang Hospital for medical treatment or consultation.
Key inclusion criteria (1 listed)
- People with heart disease, thyroid disease, breast disease, and liver disease.
Key exclusion criteria (1 listed)
- Someone who has received surgery on the target organ in question.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Quantitative ultrasound information
Time frame: 5 years
Quantitative ultrasound images of heart, thyroid, and breast disease
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 May 2023.
- Results publication: PubMed 27896451Cheng 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.
- Results publication: PubMed 28695342Chi 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.
- Results publicationF. 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.
- Results publication: PubMed 28186630Ma 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.
- Results publicationChen 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.
- Results publication: PubMed 27893402Lekadir 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.
Related trials for Chronic Liver Disease
Not yet recruitingPhase 4
B-Ensure: Long-term Follow-up Study to Evaluate Durability of Treatment Response in Previous Bepirovirsen Study Participants
Hepatitis B
Not yet recruitingObservational study
Liver Stiffness Changes by iLivTouch to Predict Liver-Related Events in MAFLD
MAFLD
Active, not recruitingPhase 2
A Clinical Study of HT-101 and HT-102 in Patients With Chronic Hepatitis B Virus Infection
Chronic Hepatitis B
Active, not recruitingPhase 1/2
A Clinical Study of HT-101 and/or HT-102 in Patients With Chronic Hepatitis B Virus Infection
Chronic Hepatitis B
RecruitingObservational study
Cancer Experience Registry (CER) for Cancer Patients and Caregivers
Neoplasms, Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chronic Liver Disease and Thyroid Disease, run by Seoul National University Bundang Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 1 May 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Quantitative ultrasound information (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05836246, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 May 2023.