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

NCT05888935 | Recruiting

• 1 other identifier: 2023-04Obs-CHRMT
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

tomography; dental injury; artificial intelligence; periapical lesions

Outcome Measures

Primary Outcomes (1)

• Artificial Intelligence software performance

  measurement of the F1 score. The F1 score is calculated as the harmonic mean of the precision and recall scores. It ranges from 0-100%, and a higher F1 score denotes a better quality classifier.

  2 years

Secondary Outcomes (1)

• Artificial Intelligence software specificity

  2 years

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with periapical lesions

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.

Sponsors & Collaborators

• Centre Hospitalier Régional Metz-Thionville: lead

Study Sites (1)

CHR Metz-Thionville/Hopital de Mercy

Metz, 57085, France

RECRUITING

MeSH Terms

Conditions

Periapical Diseases

Condition Hierarchy (Ancestors)

Jaw Diseases; Stomatognathic Diseases; Periodontal Diseases; Mouth Diseases

Study Officials

• Marc ENGELS-DEUTSCH, MD

  CHR Metz Thionville Hopital de Mercy

  PRINCIPAL INVESTIGATOR

• Paul RETIF, MD, PhD

  CHR Metz Thionville Hopital de Mercy

  STUDY CHAIR

Central Study Contacts

Arpiné EL NAR, PhD

CONTACT

0033387557766; a.elnar@chr-metz-thionville.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 25, 2023
• First Posted: June 5, 2023
• Study Start: October 1, 2022
• Primary Completion: December 1, 2024
• Study Completion: December 1, 2024
• Last Updated: August 9, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)