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 Type
OBSERVATIONAL
Enrollment
16,000
Based on the previously developed X-ray image diagnosis and report generation model, radiologists are assisted in interpreting X-ray images and generating reports.
After the patient undergoes an X-ray examination, a radiologist generates the report and makes the diagnosis.
Wuhan Union Hospital
Wuhan, Hubei, China
Wuhan Union Jinyin Lake Hospital
Wuhan, Hubei, China
Wuhan Union West Hospital
Wuhan, Hubei, China
The First Affiliated Hospital of Zhengzhou University
Zhengzhou, China
Area Under the Curve
The primary outcome was the AUC to evaluate diagnostic performance, comparing radiologists with and without AI assistance.
Time frame: From enrollment to the end of X-ray image acquisition at 1 week
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.
Time frame: From enrollment to the end of X-ray image acquisition at 1 week
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