Clinical trial NCT05888935: Detection of Periapical Lesions on Dental Panoramic Radiographs Based on Artificial Intelligence
Official title: Detection of Periapical Lesions on Dental Panoramic Images Based on Artificial Intelligence Using Cone Beam Computed Tomography
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Periapical Diseases
Keywords: tomography
dental injury
artificial intelligence
periapical lesions
Lead sponsor: Centre Hospitalier Régional Metz-Thionville
Sponsor class: OTHER
First posted: 2023-06-05
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with periapical lesions

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).

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Artificial Intelligence software performance
timeFrame: 2 years
Secondary outcomes:
measure: Artificial Intelligence software specificity
timeFrame: 2 years
Other outcomes:
Locations:
city: Metz
country: France
facility: CHR Metz-Thionville/Hopital de Mercy
Study officials:
affiliation: CHR Metz Thionville Hopital de Mercy
name: Marc ENGELS-DEUTSCH, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: CHR Metz Thionville Hopital de Mercy
name: Paul RETIF, MD, PhD
role: STUDY_CHAIR
References: