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AI trained to spot dead jawbone on routine dental X-Rays

NCT ID NCT07809048

Diagnosis Sponsor: Izmir Katip Celebi University Source: ClinicalTrials.gov ↗

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
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 This study
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 Results may not be published yet.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
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Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Sep 09, 2026 · Last updated Sep 10, 2026 · Updated 1 time

Summary

Researchers at Izmir Katip Celebi University are testing a deep learning model called UNet to automatically outline bone sequestra, pieces of dead jawbone, on panoramic dental X-rays. The study uses 120 anonymized X-rays with 134 marked lesions from patients who had radiotherapy, bisphosphonate or antiangiogenic drug use, trauma, or osteomyelitis. The goal is to see whether the model can match expert markings and reduce the time and disagreement involved in reading these images.

What this could mean

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

Active substance
a UNet-based deep learning model for segmenting bone sequestra on panoramic dental X-rays
What this could lead to
If it works, dentists and surgeons could get faster, more consistent outlines of dead jawbone on routine panoramic X-rays, which may help them plan treatment for osteomyelitis, osteoradionecrosis, and medication-related osteonecrosis of the jaw.
What could go wrong
This is a small, single-center study using 120 archived X-rays, so the model may not perform as well on images from other clinics or with different equipment. It is a diagnostic aid, not a treatment, and it still needs validation in real clinical settings before it could be trusted in practice.

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

120 people

The number who actually took part.

Started

Mar 2024

Finished

Mar 2025

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

Patients will be selected among the patients with osteomyelitis with sequestrum

Ages

18 to 99 years

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: * Patient with exposed bone over 8 weeks with a history of bisphosphonates or antiangiogenic drugs, * Patient with a history of previous radiotherapy, * Patients with necrotic exposed bone with or without a history of trauma * Patients with osteomyelitis. Exclusion Criteria: * Patients with no clear vision of the borders of the sequestra, * Panoramic images with artifacts in and around the lesion.

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Conditions

The condition(s) this trial relates to.

osteomyelitis osteonecrosis osteonecrosis of the jaw osteoradionecrosis

As listed by the trial registrant

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

Patient with A history of previous radiotherapy

Contacts and locations

Locations

  • Izmir Katip Celebi University

    Izmir, Çiğli, 35640, Turkey (Türkiye)

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