Ultrasound Image InterpretationNCT04876157
Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
A study testing Artificial intelligence-aimed point-of-care ultrasound image interpretation system in people with Ultrasound Image Interpretation, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for… Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Ultrasound Image Interpretation
- What is being tested?
- Artificial intelligence-aimed point-of-care ultrasound image interpretation system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Aug 2020to 31 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- patients receiving echocardiography or renal ultrasound
Who cannot join
- patients not receiving echocardiography or renal ultrasound
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
Wan-Ching Lien
Taipei, None Selected, 100
Wan-Ching Lien, +886223123456, wanchinglien@ntu.edu.tw
About this study
This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.
Read the detailed description
Ultrasound is a non-invasive and non-radiated diagnostic tool in the emergency and critical care settings. In clinical practice, timely interpretation of sonographic images to facilitate decision-making is essential. However, it depends on operators' experience. As usual, it takes time for junior emergency physicians to have good diagnostic accuracy through traditional sonographic education. How to shorten the learning This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.
This pioneer study can provide two AI-assisted ultrasound image recognition systems in the real clinical conditions. They can experience of clinical applications and contribute to current medical education. Moreover, it can improve decision-making process and quality of care in the emergency and critical care units. Furthermore, the set-up models can be used in other target ultrasound image recognition in the future.
- Conditions
- Ultrasound Image Interpretation
- Being tested
- Artificial intelligence-aimed point-of-care ultrasound image interpretation system
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence-aimed point-of-care ultrasound image interpretation systemDiagnostic test
improve the sensitivity and specificity of the AI-aimed ultrasound interpretation system
| Study group | Role | What they receive |
|---|---|---|
| Artificial intelligence-aimed ultrasound image interpretation | Experimental | Not described Receives: Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system |
What is being measured
The primary outcome is the main question the study is designed to answer.
sensitivity and specificity of AI interpretation
increase the sensitivity and specificity of AI to interpret the ultrasound image
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 May 2021
- Study start1 Aug 2020
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ultrasound Image InterpretationStudies of the same condition, open and under way.
- More trials run by National Taiwan University 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Wan-Ching Lien, Principal investigator, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04876157
- Sponsor's protocol ID
- 202006124RINC
- Status last verified
- Oct 2026
- First submitted
- 2 May 2021
- Last updated
- 21 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
- ClinicalTrials.gov ID
- NCT04876157
- Acronym / sponsor ID
- 202006124RINC
- Conditions
- Ultrasound Image Interpretation
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 1 Aug 2020 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 6 May 2021 (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.
This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.
Detailed description
Ultrasound is a non-invasive and non-radiated diagnostic tool in the emergency and critical care settings. In clinical practice, timely interpretation of sonographic images to facilitate decision-making is essential. However, it depends on operators' experience. As usual, it takes time for junior emergency physicians to have good diagnostic accuracy through traditional sonographic education. How to shorten the learning This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.
This pioneer study can provide two AI-assisted ultrasound image recognition systems in the real clinical conditions. They can experience of clinical applications and contribute to current medical education. Moreover, it can improve decision-making process and quality of care in the emergency and critical care units. Furthermore, the set-up models can be used in other target ultrasound image recognition in the future.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Artificial intelligence-aimed ultrasound image interpretation | Experimental | Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- patients receiving echocardiography or renal ultrasound
Key exclusion criteria (1 listed)
- patients not receiving echocardiography or renal ultrasound
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence-aimed point-of-care ultrasound image interpretation systemimprove the sensitivity and specificity of the AI-aimed ultrasound interpretation system | Diagnostic test | Artificial intelligence-aimed ultrasound image interpretation |
Primary outcome
sensitivity and specificity of AI interpretation
Time frame: 6 months
increase the sensitivity and specificity of AI to interpret the ultrasound image
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Oct 2026.
- Individual participant dataSharing planned: No
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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.
Wan-Ching Lien principal investigator
National Taiwan University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence-aimed point-of-care ultrasound image interpretation system in people with Ultrasound Image Interpretation, run by National Taiwan University Hospital at 1 site in Taiwan.
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: looking for participants now, as of the sponsor's last update on 21 Oct 2026. 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 Taiwan. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wan-Ching Lien, Ph D (+886-2-23123456, wanchinglien@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
sensitivity and specificity of AI interpretation (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 21 Oct 2026.