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X-ray Assisted Diagnostic System

NCT07497243 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 16000

Last updated 2026-03-27

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

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.

DIAGNOSTIC_TEST

Radiologist diagnostic group

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

Sponsors & Collaborators

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

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-01
Primary Completion
2026-10-31
Completion
2026-11-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07497243 on ClinicalTrials.gov