Detection of Periapical Lesions on Dental Panoramic Radiographs Based on Artificial Intelligence

Recruiting now

Conditions studied: Periapical Diseases

In brief

Dental periapical damages can have various reasons and is reflected by a radiolucent lesion on complementary imaging: angulated retro-alveolar (RA) radiographs, dental panoramic radiographs, and three-dimensional imaging such as computed tomography (CT) or cone-beam computed tomography (CBCT). For the radiographic detection of these deep periodontal lesions, the dental panoramic represents a first approach commonly performed with relatively low radiation. The investigation can be followed by retroalveolar radiology imaging that are more localized and more precise. However, using these techniques, the detection rates of these lesions are low (20% and 36% respectively), it is necessary to use three-dimensional tomographic investigation to be more discriminating (69%). The gold standard imaging for detection of these lesions is CBCT followed by retroalveolar radiography (\~2x less sensitive than CBCT) and panoramic radiography (\~2x less sensitive than RA). Although not a full-thickness radiograph, the dental panoramic has the advantage of being more commonly performed while being less radiating than CBCT and giving a global view of the dental arches on a single image. The detection of periapical lesions is done after a clinical assessment and a visual appreciation of the complementary examinations. The aim of this project is to improve the detection of periapical lesions, by developing an algorithm able to identify them on a panoramic dental radiograph. This algorithm is based on a deep learning system trained with reference data including panoramic dental imaging and CBCT with an acquisition interval of less than 3 months. The model is based on a previous work, will improve the quality of the initial data (using CBCT), using innovative artificial intelligence algorithms (transfer learning).

Key facts

Study ID

NCT05888935

Run by

Centre Hospitalier Régional Metz-Thionville

People needed

2000

Starts

2022-10-01

Expected to finish

2027-12-01

Last updated by the study team

2026-06-24

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Patients who have had CBCT and panoramic dental imaging with less than 3 months between the two examinations

You may not qualify if…

• Patients who refused to participe in the study.

Where it is running

• CHR Metz-Thionville/Hopital de Mercy — Metz, France (enrolling)

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.