An observational study in Pneumothorax, Intestinal Obstruction and Spondylolysis, sponsored by The Third Affiliated Hospital of Southern Medical University. Not yet recruiting. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-15.
Sponsored by The Third Affiliated Hospital of Southern Medical University · Observational
This study tests whether an artificial intelligence (AI) tool helps radiologists read X-rays more accurately.
Radiologists read X-rays to find diseases such as pneumonia, bone fractures, and abdominal problems. AI is a computer program trained on many past X-rays. It may help radiologists spot diseases faster and more correctly. This study compares reading X-rays with the AI tool to reading X-rays without it.
Who takes part
What happens
Possible benefits and risks
This is a multi-reader multi-case (MRMC) crossover diagnostic accuracy study using retrospective, de-identified X-ray images. Ten radiologists read the same set of cases under two conditions - unassisted reading and AI-assisted reading - in random order (AB/BA sequence), with a four-week washout between stages. Each radiologist serves as their own control. No patients are prospectively enrolled, and no new imaging procedures are performed.
179 studies on the registry are indexed under Pneumothorax; 42 are open to participants now.
This study's planned enrollment of 500 is above the median of 122 across 59 observational studies indexed under Pneumothorax.
Browse Pneumothorax studies →The Third Affiliated Hospital of Southern Medical University is the lead sponsor of 11 studies on the registry; 5 are open to participants now.
Counted across the registry records on this site, refreshed daily.
This study enrolls 10 radiologists from multiple hospitals as readers. They read retrospective, de-identified X-ray images of the chest, abdomen, and lumbar spine, collected from participating hospital image archives and public datasets. The image cases are from adult patients (age 18 years or older) with common X-ray findings across chest, abdominal, and spinal diseases. No patients are prospectively enrolled; all images are reused from routine clinical care after de-identification.
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Exclusion Criteria:
Radiologists read X-ray images using their usual clinical workflow, without access to any AI tool output. They record their diagnosis, confidence level, and reading time.
Radiologists read the same X-ray images while having access to the output of an X-ray imaging AI tool, including its diagnostic suggestions and predicted probabilities. The radiologist reviews the AI output and makes the final diagnosis, then records confidence level and reading time.
Macro-averaged Diagnostic Accuracy of Radiologists' Single-Disease Binary Detection (Balanced Accuracy）
Diagnostic performance of radiologists on single-disease binary detection tasks for four common X-ray diseases. Accuracy is quantified as macro-averaged balanced accuracy (B-ACC) across the four diseases, comparing unaided reading versus AI-assisted reading. Each binary decision is compared against an expert-adjudicated reference standard established from clinical, imaging, and follow-up information.
Time frame: Baseline (Stage 1 reading) and Week 4 (Stage 2 reading, after a 4-week washout period)
Per-disease Sensitivity and Specificity
Disease-specific sensitivity and specificity of radiologists' binary diagnostic decisions under unaided versus AI-assisted conditions, estimated using generalized linear mixed-effects models.
Time frame: Baseline (Stage 1 reading) and Week 4 (Stage 2 reading, after a 4-week washout period)
Single-Disease Diagnostic Reading Time
Time in seconds from case presentation to submission of the binary diagnostic decision, recorded by an independent timer per case. Compared between unaided and AI-assisted reading conditions.
Time frame: Baseline (Stage 1 reading) and Week 4 (Stage 2 reading, after a 4-week washout period)
Inter-Reader Diagnostic Agreement (Kappa)
Inter-reader consistency of binary diagnostic decisions among the 10 participating radiologists, assessed by Kappa statistics, under unaided versus AI-assisted conditions.
Time frame: Baseline (Stage 1 reading) and Week 4 (Stage 2 reading, after a 4-week washout period)
Rate of Diagnostic Decision Changes With AI Assistance
Proportion of cases for which each radiologist's binary decision differed between the unaided session and the AI-assisted session, classified as correct-to-incorrect or incorrect-to-correct changes.
Time frame: Baseline (Stage 1 reading) and Week 4 (Stage 2 reading, after a 4-week washout period)
No study locations are listed for this record.
Plan to share: Yes
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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The Third Affiliated Hospital of Southern Medical University