Periapical DiseasesOPTITOMONCT05888935
Detection of Periapical Lesions on Dental Panoramic Radiographs Based on Artificial Intelligence
An observational study of Periapical Diseases, run by Centre Hospitalier Régional Metz-Thionville at 1 site in France.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Periapical Diseases
- How many people
- 2,000 participants
- Where
- 1 siteFrance
- Study dates
- 1 Oct 2022to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Patients with periapical lesions
Who may be able to join
- Patients who have had CBCT and panoramic dental imaging with less than 3 months between the two examinations
Who cannot join
- Patients who refused to participe in the study.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site, 1 recruiting
- Recruiting
CHR Metz-Thionville/Hopital de Mercy
Metz, 57085
Arpiné EL NAR, PhD, 0033387557766, a.elnar@chr-metz-thionville.fr
projet-recherche@chr-metz-thionville.fr
About this study
Official title: 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.
Read more
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).
Read the 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.
- Conditions
- Periapical Diseases
- Keywords
- tomography, dental injury, artificial intelligence, periapical lesions
- Medical subject headings
- Periapical Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2 years
Secondary outcomes (1)
Artificial Intelligence software specificity
measurement of the true positives, true negatives, false positives, false negatives
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jun 2023
- Study start1 Oct 2022
- Primary completion1 Dec 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Periapical DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Régional Metz-Thionville
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Marc ENGELS-DEUTSCH, MD, Principal investigator, CHR Metz Thionville Hopital de MercyPaul RETIF, MD, PhD, Study chair, CHR Metz Thionville Hopital de Mercy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05888935
- Sponsor's protocol ID
- 2023-04Obs-CHRMT
- Acronym
- OPTITOMO
- Status last verified
- Jun 2026
- First submitted
- 25 May 2023
- Last updated
- 24 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Periapical Lesions on Dental Panoramic Images Based on Artificial Intelligence Using Cone Beam Computed Tomography
- ClinicalTrials.gov ID
- NCT05888935
- Acronym / sponsor ID
- OPTITOMO / 2023-04Obs-CHRMT
- Conditions
- Periapical Diseases
- Keywords
- tomography, dental injury, artificial intelligence, periapical lesions
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Centre Hospitalier Régional Metz-ThionvilleUniversity, hospital or other
- Study start
- 1 Oct 2022 (actual)
- Primary completion
- 1 Dec 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 5 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with periapical lesions
Key inclusion criteria (1 listed)
- Patients who have had CBCT and panoramic dental imaging with less than 3 months between the two examinations
Key exclusion criteria (1 listed)
- Patients who refused to participe in the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Artificial Intelligence software performance
Time frame: 2 years
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.
1 secondary outcome
Artificial Intelligence software specificity
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jun 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Marc ENGELS-DEUTSCH, MD principal investigator
CHR Metz Thionville Hopital de Mercy
Paul RETIF, MD, PhD study chair
CHR Metz Thionville Hopital de Mercy
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Periapical Diseases, run by Centre Hospitalier Régional Metz-Thionville at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Arpiné EL NAR, PhD (0033387557766, a.elnar@chr-metz-thionville.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial Intelligence software performance (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Jun 2026.