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Digital doubles of patient airways could speed up bronchoscopy training

NCT ID NCT07718451

Recruiting now Knowledge-focused Sponsor: Gang Hou Source: ClinicalTrials.gov ↗

First seen Jul 22, 2026 · Last updated Jul 23, 2026 · Updated 1 time

Summary

This trial tests whether training novice bronchoscopists using computer-generated 'digital twin' models of real patient airways — showing a wide range of normal anatomical variations — can help them learn the procedure faster than traditional training with a single, uniform airway model. About 110 trainees with little to no prior bronchoscopy experience will be randomly assigned to one of the two training methods. Their early clinical performance will be measured over their first 30 supervised procedures to see if the digital twin group shows a steeper learning curve.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance
multi-patient CT-derived digital twin anatomical variability training
What this could lead to
If effective, this training method could help novice doctors learn bronchoscopy faster and more safely, potentially improving patient outcomes.
What could go wrong
This is a relatively small, early-stage trial. The training may not translate to real-world clinical performance, and benefits might not be large enough to justify the cost and effort.

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Conditions

The condition(s) this trial relates to.

learning disability

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

BRONCHOSCOPY LEARNING CURVE LEARNING CURVES AND OUTCOMES OF SIMULATION-BASED TRAINING

Contacts and locations

Study contacts

  • Contact

    Phone: •••-•••-•••• Email: •••••@•••••

  • Contact

    Phone: •••-•••-•••• Email: •••••@•••••

Locations

  • China-Japan Friendship Hospital

    RECRUITING

    Beijing, Beijing Municipality, 100029, China

    Contact Phone: •••-•••-•••• Email: •••••@•••••

    Contact

    Contact

    Contact Phone: •••-•••-•••• Email: •••••@•••••

    Contact

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