Detection of Periapical Lesions on Dental Panoramic Radiographs Based on Artificial Intelligence (OPTITOMO)

Detection of Periapical Lesions on Dental Panoramic Images Based on Artificial Intelligence Using Cone Beam Computed Tomography

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).

Study Overview

• Status: Recruiting
• Conditions: Periapical Diseases

Detailed Description

The final objective of the research is to improve the early diagnosis of periapical lesions, which would allow a better and faster care of these lesions namely at early stages. This represents a major public health interest since these lesions can be responsible for multiple local and regional pathologies (osteomyelitis, cervico-facial cellulitis, thrombophlebitis, cerebral abscesses...) or even more serious general pathologies (cardiac pathologies, cardiovascular diseases, diabetes, renal diseases, tendinopathies...). For certain target groups such as the military and high-level athletes, this research would make it possible to improve the assessment carried out before medical aptitude or club transfer.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Arpiné EL NAR, PhD; Phone Number: 0033387557766; Email: a.elnar@chr-metz-thionville.fr

Study Locations

• France
  • Metz, France, 57085
    • Recruiting
    • CHR Metz-Thionville/Hopital de Mercy
    • Contact: Email: projet-recherche@chr-metz-thionville.fr
    • Contact: Arpiné EL NAR, PhD; Phone Number: 0033387557766; Email: a.elnar@chr-metz-thionville.fr
    • Sub-Investigator: Paul RETIF, MD
    • Principal Investigator: Marc ENGELS-DEUTSCH, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Patients with periapical lesions

Description

Inclusion Criteria:

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

Exclusion Criteria:

• Patients who refused to participe in the study.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Artificial Intelligence software specificity	measurement of the true positives, true negatives, false positives, false negatives	2 years

Collaborators and Investigators

• Sponsor: Centre Hospitalier Régional Metz-Thionville
• Investigators: Principal Investigator: Marc ENGELS-DEUTSCH, MD, CHR Metz Thionville Hopital de Mercy; Study Chair: Paul RETIF, MD, PhD, CHR Metz Thionville Hopital de Mercy

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2022
• Primary Completion (Estimated): December 1, 2024
• Study Completion (Estimated): December 1, 2024

Study Registration Dates

• First Submitted: May 25, 2023
• First Submitted That Met QC Criteria: May 25, 2023
• First Posted (Actual): June 5, 2023

Study Record Updates

• Last Update Posted (Actual): June 7, 2023
• Last Update Submitted That Met QC Criteria: June 5, 2023
• Last Verified: May 1, 2023

More Information

Terms related to this study

• Keywords: artificial intelligence; tomography; dental injury; periapical lesions
• Additional Relevant MeSH Terms: Stomatognathic Diseases; Periodontal Diseases; Mouth Diseases; Jaw Diseases; Periapical Diseases
• Other Study ID Numbers: 2023-04Obs-CHRMT

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No