Cure My Disease

Don't miss your cure!

 Log in / Sign up

AI listens to the diaphragm to spot breathing pauses in premature babies

NCT ID NCT07794007

Diagnosis Sponsor: King's College Hospital NHS Trust Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting This study
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Aug 31, 2026 · Last updated Sep 01, 2026 · Updated 1 time

Summary

This study tests whether a machine-learning model can automatically detect pauses in breathing, called apnoea, in preterm infants born before 32 weeks of gestation. The model uses signals from the diaphragm muscle, along with airway pressure and oxygen levels, recorded over eight hours. Researchers will compare the model's detections with nursing charts to see if it identifies more apnoea episodes and correctly classifies them as central or obstructive. Better classification could help doctors understand the cause of each pause and choose the right treatment.

What this could mean

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

Active substance
A machine-learning algorithm that analyzes diaphragmatic electromyography (dEMG) signals to detect and classify apnoea episodes
What this could lead to
If the algorithm works, it could help clinicians identify apnoea causes more accurately and tailor interventions for preterm infants.
What could go wrong
This is a small observational study, so the algorithm may not perform as expected in real-world settings. The results may not apply to all preterm infants or all types of breathing support.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 30 people

The number the study aims to enrol. It can still change while the study runs.

Expected to start

Sep 2026

An estimate. Start dates often move.

Expected to finish

Jul 2027

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

Infants admitted and cared for at the Neonatal Intensive Care Unit at King's College Hospital

Ages

Up to 36 weeks

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: Preterm infants \<32 weeks of gestation at birth and up to 36 weeks postmenstrual age, on non-invasive respiratory support including: * non-invasive positive pressure ventilation (NIPPV) * nasal continuous positive airway pressure (CPAP) * heated humidified high flow nasal cannula (HHFNC) oxygen, either as primary or post extubation respiratory support. Exclusion Criteria: * Infants born above 32 weeks of gestation. * Infants with known major congenital abnormalities. * Infants above 36 weeks postmenstrual age (PMA). * Non-English speakers.

Get updates

Get notified about this study

Sign up to get updates when this study changes or when new studies for APNOEA are added.

Our safety recommendation!

By submitting, you agree to our Terms of use

Conditions

The condition(s) this trial relates to.

Apnea central sleep apnea syndrome Premature Birth

As listed by the trial registrant

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

APNOEA Prematurity

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  2. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

More trials for these conditions

Other studies related to the condition(s) this trial covers.