MedTrace Logo

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

NCT05888935 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2024-08-09

No results posted yet for this study

Summary

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).

Conditions

  • Periapical Diseases

Sponsors & Collaborators

  • Centre Hospitalier Régional Metz-Thionville

    lead OTHER

Principal Investigators

  • Marc ENGELS-DEUTSCH, MD · CHR Metz Thionville Hopital de Mercy

  • Paul RETIF, MD, PhD · CHR Metz Thionville Hopital de Mercy

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-10-01
Primary Completion
2024-12-01
Completion
2024-12-01

Countries

  • France

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05888935 on ClinicalTrials.gov