X-ray Assisted Diagnostic System

NCT07497243 | Not Yet Recruiting DMC

• 1 other identifier: X-Ray-001
• Study Type: observational
• Target: 16,000
• Locations: 1 country
• Sites: 4

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 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

Keywords

X-Ray; AI; Chest diseases; Accuracy

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• X-ray report generation time

  From enrollment to the end of X-ray image acquisition at 1 week

Other Outcomes (1)

• Radiologist score

  From enrollment to the end of X-ray image acquisition at 4 weeks

Study Arms (2)

Radiologist diagnostic group

After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.

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.

Diagnostic Test: AI-assisted radiologist diagnostic group

Interventions

AI-assisted radiologist diagnostic group DIAGNOSTIC_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.

AI-assisted radiologist diagnostic group

Radiologist diagnostic group DIAGNOSTIC_TEST

After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.

Radiologist diagnostic group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients scheduled to undergo X-ray examination

You may qualify if:

• 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)

You may not qualify if:

• Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
• Pregnant or lactating women

Sponsors & Collaborators

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: lead

Study Sites (4)

Wuhan Union Hospital

Wuhan, Hubei, 430022, China

Location

Wuhan Union Jinyin Lake Hospital

Wuhan, Hubei, 430022, China

Location

Wuhan Union West Hospital

Wuhan, Hubei, 430022, China

Location

The First Affiliated Hospital of Zhengzhou University

Zhengzhou, China

Location

Central Study Contacts

Huangxuan Zhao, PhD

CONTACT

18971676985; zhao_huangxuan@sina.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 4 Weeks
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 22, 2026
• First Posted: March 27, 2026
• Study Start: May 1, 2026
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): November 30, 2026
• Last Updated: March 27, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (4)