Abdominal DiseasesRADARNCT07040358
Rapid Abdominal Diagnosis With AI & Radiology
An observational study of Abdominal Diseases, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
Trial summary
This study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Abdominal Diseases
- How many people
- 2,000,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2023to Jun 2026
- Phase
- Observational
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: This study uses a retrospective multicenter cohort comprising approximately 2 million cases of multiphase contrast-enhanced abdominal CT scans. All included imaging data are paired with corresponding radiology reports. The dataset reflects real-world imaging scenarios of various abdominal diseases.
Who may be able to join
- multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images
Who cannot join
- CT images with poor diagnostic quality due to artifacts, including but not limited to: Convolution artifacts caused by improper arm positioning (e.g., arms placed alongside the body instead of above the head),Respiratory motion artifacts due to inadequate breath-holding.
Trial locations and contacts
1 site in 1 country.
China1 site
the First Affliated Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, 310003
About this study
Official title: Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images
This study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space alignment, hybrid reasoning, and multimodal report generation. The study includes the following components: (1) construction of a dual-modality foundation model to align abdominal CT features with corresponding radiology reports; (2) development of a model to standardize CT phase variation among patients; and (3) creation of an automated image interpretation and reporting system that integrates multi-source clinical data. The effectiveness of the system will be evaluated through a report quality assessment framework and clinical validation. This project aims to improve the accuracy and clinical applicability of automated abdominal disease interpretation and promote intelligent innovation in healthcare delivery.
- Conditions
- Abdominal Diseases
- Keywords
- Artificial Intelligence, abdominal diseases, contrast-enhanced CT, RADAR
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Internal Training Set | Not described | |
| Internal Validation Set | Not described | |
| External Test Set | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT
The primary outcome is the overall performance of the AI model in detecting and characterizing lesions in abdominal organs using multiphase contrast-enhanced CT scans. Performance will be measured using area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity, with expert radiologist consensus reports as the reference standard.
Measured: After internal and external validation datasets are processed (estimated 6-12 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2025
- Study start1 Dec 2023
- Primary completion30 Sep 2025
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Abdominal DiseasesStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Zhejiang UniversityThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Zhejiang University
- Sponsor type
- University, hospital or other
- Collaborators
- the First Division Hospital of Xinjiang Production and Construction Corps, The First People's Hospital of Yuhang District, Affiliated Hospital of Jiaxing University, Jixi County People's Hospital, Anji County People's Hospital, Zhejiang University, People's Hospital of Beilun District, Ningbo City, Haining People's Hospital, People's Hospital of Jingning She Autonomous County
- Responsible party
- Principal investigator, Qi Zhang, First Affiliated Hospital of Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07040358
- Sponsor's protocol ID
- RADAR
- Acronym
- RADAR
- Status last verified
- Mar 2026
- First submitted
- 19 Jun 2025
- Last updated
- 5 Mar 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 5 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images
- ClinicalTrials.gov ID
- NCT07040358
- Acronym / sponsor ID
- RADAR / RADAR
- Conditions
- Abdominal Diseases
- Keywords
- Artificial Intelligence, abdominal diseases, contrast-enhanced CT, RADAR
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 2,000,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Zhejiang UniversityUniversity, hospital or other
- Collaborators
- the First Division Hospital of Xinjiang Production and Construction Corps, The First People's Hospital of Yuhang District, Affiliated Hospital of Jiaxing University, Jixi County People's Hospital, Anji County People's Hospital, Zhejiang University, People's Hospital of Beilun District, Ningbo City, Haining People's Hospital, People's Hospital of Jingning She Autonomous County
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- 30 Sep 2025 (actual)
- Study completion
- Jun 2026 (estimated)
- First posted
- 27 Jun 2025 (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 study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space alignment, hybrid reasoning, and multimodal report generation. The study includes the following components: (1) construction of a dual-modality foundation model to align abdominal CT features with corresponding radiology reports; (2) development of a model to standardize CT phase variation among patients; and (3) creation of an automated image interpretation and reporting system that integrates multi-source clinical data. The effectiveness of the system will be evaluated through a report quality assessment framework and clinical validation. This project aims to improve the accuracy and clinical applicability of automated abdominal disease interpretation and promote intelligent innovation in healthcare delivery.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Internal Training Set | – | – |
| Internal Validation Set | – | – |
| External Test Set | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This study uses a retrospective multicenter cohort comprising approximately 2 million cases of multiphase contrast-enhanced abdominal CT scans. All included imaging data are paired with corresponding radiology reports. The dataset reflects real-world imaging scenarios of various abdominal diseases.
Key inclusion criteria (1 listed)
- multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images
Key exclusion criteria (1 listed)
- CT images with poor diagnostic quality due to artifacts, including but not limited to: Convolution artifacts caused by improper arm positioning (e.g., arms placed alongside the body instead of above the head),Respiratory motion artifacts due to inadequate breath-holding.
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
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT
Time frame: After internal and external validation datasets are processed (estimated 6-12 months)
The primary outcome is the overall performance of the AI model in detecting and characterizing lesions in abdominal organs using multiphase contrast-enhanced CT scans. Performance will be measured using area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity, with expert radiologist consensus reports as the reference standard.
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 5 Mar 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. 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 Abdominal Diseases, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
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: under way, not looking for participants, as of the sponsor's last update on 5 Mar 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 China. 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?
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?
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT (time frame: After internal and external validation datasets are processed (estimated 6-12 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 5 Mar 2026.