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NCT Number: NCT07497243

X-ray Assisted Diagnostic System

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.

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Key information

Conditions

Chest X-ray for Clinical Evaluation

Sex eligibility

All sexes

Study type

Observational

Primary location

Wuhan Union Hospital, Wuhan, Hubei, China

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Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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)

Exclusion criteria

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

Treatment and study plan

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.

Radiologist diagnostic group

Diagnostic Test

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

Primary outcomes

  1. 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.

Secondary outcomes

  1. X-ray report generation time

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

    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.

Other outcomes

  1. Radiologist score

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

    Radiologist score refers to the evaluation or rating assigned by senior radiologists based on imaging findings generated by AI or AI+radiologist.

Study contacts

Contact information is provided by the study sponsor or research team.

Huangxuan Zhao, PhD

CONTACT

[email protected]

18971676985

Sponsors and collaborators

Lead sponsor

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

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Mar 27, 2026
Registry last updated
Mar 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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