Chest X-ray for Clinical EvaluationNCT07497243
X-ray Assisted Diagnostic System
An observational study of Chest X-ray for Clinical Evaluation, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 4 sites in China.
Trial summary
X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Chest X-ray for Clinical Evaluation
- What is being tested?
- AI-assisted radiologist diagnostic group and Radiologist diagnostic group
- How many people
- 16,000 participants
- Where
- 4 sitesChina
- Study dates
- 1 May 2026to 30 Nov 2026
- How long will I take part?
- 4 Weeks
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients scheduled to undergo X-ray examination
Who may be able to join
- Clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring X-ray diagnosis；
- Patients providing written informed consent for research data use；
- Complete clinical records (including chief complaints, medical history, and laboratory test results)
Who cannot join
- Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
- Pregnant or lactating women
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites
Wuhan Union Hospital
Wuhan, Hubei, 430022
Lei Chen, MD, 15971480677, chan0812@126.com
Wuhan Union Jinyin Lake Hospital
Wuhan, Hubei, 430022
Lei Chen, 15971480677, chan0812@126.com
Wuhan Union West Hospital
Wuhan, Hubei, 430022
Huangxuan Zhao, 18971676985, zhao_huangxuan@sina.com
The First Affiliated Hospital of Zhengzhou University
Zhengzhou
Huangxuan Duan, 13209867189, xuhuaduan2023@126.com
About this study
Official title: Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial
X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not meet clinical demands.
Based on this, we developed an artificial intelligence model to assist radiologists in interpreting X-ray images and generating reports, with the aim of improving diagnostic accuracy and reducing interpretation time.
- Conditions
- Chest X-ray for Clinical Evaluation
- Being tested
- AI-assisted radiologist diagnostic group, Radiologist diagnostic group
- Keywords
- X-Ray, AI, Chest diseases, Accuracy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted radiologist diagnostic groupDiagnostic test
Based on the previously developed X-ray image diagnosis and report generation model, radiologists are assisted in interpreting X-ray images and generating reports.
Radiologist diagnostic groupDiagnostic test
After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.
| Study group | Role | What they receive |
|---|---|---|
| Radiologist diagnostic group | After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis. Receives: Diagnostic Test: Radiologist diagnostic group | |
| AI-assisted radiologist diagnostic group | After the patient undergoes an X-ray examination, an AI-assisted radiologist generates the report and makes the diagnosis. Receives: Diagnostic Test: AI-assisted radiologist diagnostic group |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve
The primary outcome was the AUC to evaluate diagnostic performance, comparing radiologists with and without AI assistance.
Measured: From enrollment to the end of X-ray image acquisition at 1 week
Secondary outcomes (1)
X-ray report generation time
X-ray report generation time refers to the amount of time required to produce a diagnostic report after an X-ray examination has been performed. It typically measures the interval from when the X-ray images are acquired to when the radiologist (with or without AI assistance) completes and finalizes the report.
Measured: From enrollment to the end of X-ray image acquisition at 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Mar 2026
- Study start1 May 2026 (estimated)
- Primary completion31 Oct 2026 (estimated)
- Study completion30 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chest X-ray for Clinical EvaluationStudies of the same condition, open and under way.
- 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 16,000 participants (estimated)
- Target duration
- 4 Weeks
Sponsor and investigators
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07497243
- Sponsor's protocol ID
- X-Ray-001
- Status last verified
- Jan 2026
- First submitted
- 22 Mar 2026
- Last updated
- 27 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- 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 27 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07497243
- Acronym / sponsor ID
- X-Ray-001
- Conditions
- Chest X-ray for Clinical Evaluation
- Keywords
- X-Ray, AI, Chest diseases, Accuracy
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 16,000 participants (estimated)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 May 2026 (estimated)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 27 Mar 2026 (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.
X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not meet clinical demands.
Based on this, we developed an artificial intelligence model to assist radiologists in interpreting X-ray images and generating reports, with the aim of improving diagnostic accuracy and reducing interpretation time.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 4 Weeks
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Radiologist diagnostic group | – | Diagnostic Test: Radiologist diagnostic groupAfter the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis. |
| AI-assisted radiologist diagnostic group | – | Diagnostic Test: AI-assisted radiologist diagnostic groupAfter the patient undergoes an X-ray examination, an AI-assisted radiologist generates the report and makes the diagnosis. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients scheduled to undergo X-ray examination
Key inclusion criteria (3 listed)
- Clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring X-ray diagnosis；
- Patients providing written informed consent for research data use；
- Complete clinical records (including chief complaints, medical history, and laboratory test results)
Key exclusion criteria (2 listed)
- Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
- Pregnant or lactating women
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 |
|---|---|---|
| AI-assisted radiologist diagnostic groupBased on the previously developed X-ray image diagnosis and report generation model, radiologists are assisted in interpreting X-ray images and generating reports. | Diagnostic test | AI-assisted radiologist diagnostic group |
| Radiologist diagnostic groupAfter the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis. | Diagnostic test | Radiologist diagnostic group |
Primary outcome
Area Under the Curve
Time frame: From enrollment to the end of X-ray image acquisition at 1 week
The primary outcome was the AUC to evaluate diagnostic performance, comparing radiologists with and without AI assistance.
1 secondary outcome
X-ray report generation time
Time frame: From enrollment to the end of X-ray image acquisition at 1 week
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 27 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.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chest X-ray for Clinical Evaluation, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 4 sites 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 3 inclusion and 2 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: opening soon, as of the sponsor's last update on 27 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 4 sites 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?
Contact Huangxuan Zhao, PhD (18971676985, zhao_huangxuan@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (time frame: From enrollment to the end of X-ray image acquisition at 1 week). 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 27 Mar 2026.