beta
Trial Radar AI
Clinical Trial NCT07497243 for Chest X-ray for Clinical Evaluation is not yet recruiting. See the Trial Radar Card View and AI discovery tools for all the details. Or ask anything here.
One study matched filter criteria
Card View
Back to Search

X-ray Assisted Diagnostic System 16,000

Not yet recruiting
Clinical Trial NCT07497243 is an observational study for Chest X-ray for Clinical Evaluation and is currently not yet recruiting. Enrollment is planned to begin on 1 May 2026 and continue until the study accrues 16,000 participants. Led by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, this study is expected to complete by 30 November 2026. The latest data from ClinicalTrials.gov was last updated on 27 March 2026.
Brief 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. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not me...Show More
Official Title

Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial

Conditions
Chest X-ray for Clinical Evaluation
Other Study IDs
  • X-Ray-001
NCT ID Number
NCT07497243
Start Date (Actual)
2026-05-01
Last Update Posted
2026-03-27
Completion Date (Estimated)
2026-11-30
Enrollment (Estimated)
16,000
Study Type
Observational
Status
Not yet recruiting
Keywords
X-Ray
AI
Chest diseases
Accuracy
Arms / Interventions
Participant Group/ArmIntervention/Treatment
Radiologist diagnostic group
After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.
Radiologist diagnostic group
After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.
AI-assisted radiologist diagnostic group
After the patient undergoes an X-ray examination, an AI-assisted radiologist generates the report and makes the diagnosis.
AI-assisted radiologist diagnostic group
Based on the previously developed X-ray image diagnosis and report generation model, radiologists are assisted in interpreting X-ray images and generating reports.
Primary Outcome Measures
Outcome MeasureMeasure DescriptionTime Frame
Area Under the Curve
The primary outcome was the AUC to evaluate diagnostic performance, comparing radiologists with and without AI assistance.
From enrollment to the end of X-ray image acquisition at 1 week
Secondary Outcome Measures
Outcome MeasureMeasure DescriptionTime Frame
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.
From enrollment to the end of X-ray image acquisition at 1 week
Participation Assistant
Eligibility Criteria

Eligible Ages
Child, Adult, Older Adult
Eligible Sexes
All
Accepts Healthy Volunteers
Yes
  • 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)

  • Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
  • Pregnant or lactating women
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology logoUnion Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study Central Contact
Contact: Huangxuan Zhao, PhD, 18971676985, [email protected]
4 Study Locations in 1 Countries

Hubei

Wuhan Union Hospital, Wuhan, Hubei, 430022, China
Lei Chen, MD, Contact, 15971480677, [email protected]
Wuhan Union Jinyin Lake Hospital, Wuhan, Hubei, 430022, China
Lei Chen, Contact, 15971480677, [email protected]
Wuhan Union West Hospital, Wuhan, Hubei, 430022, China
Huangxuan Zhao, Contact, 18971676985, [email protected]
The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Huangxuan Duan, Contact, 13209867189, [email protected]