Oral HealthNCT06258798
The Use of Artificial Intelligence in the Dental X-rays Analysis
An observational study of Oral Health and Dental Caries, run by Hospital of the Ministry of Interior, Kielce, Poland at 1 site in Poland.
Trial summary
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, taking into account the location. Read more about this study
- Who is it for?
- Ages 11 years and older
- Conditions the study is for
- Oral Health, Dental Caries, Periapical Diseases
- What is being tested?
- Taking a dental X-ray
- Type of treatment
- Surgery or procedure
- How many people
- 1,025 participants
- Where
- 1 sitePoland
- Study dates
- 1 Jan 2024to 1 Nov 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 11 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients included in the study were admitted to the radiology department in Kielce, a city in southern Poland with around 200.000 inhabitants.
Who may be able to join
- Indications for dental X-ray confirmed by a written referral from the dentist or physician (both screening tests and tests performed for treatment purposes were allowed)
- Permanent dentition (after exfoliation is completed)
Who cannot join
- Patients with mixed dentition (exfoliation has not finished)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, taking into account the location.
Read the detailed description
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, considering the location. Patients with indications for dental X-ray confirmed by a written referral and with permanent dentition will participate in the study. Then, the X-rays will be analyzed by the dentists and the AI-based software after the data has been anonymized. The results will be compared to determine the AI algorithm's sensitivity, specificity, and precision.
- Conditions
- Oral Health, Dental Caries, Periapical Diseases, Tooth Loss
- Being tested
- Taking a dental X-ray
- Medical subject headings
- Dental Caries, Periapical Diseases, Tooth Loss
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Taking a dental X-rayRadiation
Dental X-rays taken in patients with indications confirmed by a written referral.
| Study group | Role | What they receive |
|---|---|---|
| One group of patients (double gate) | Study design:
* Direction of data collection: retrospective
* Number of gates (sets of eligibility criteria): double gate (AI, human)
* Participant sampling method: Consecutive
* Method of allocating participants to index tests: Each participant received all index tests
* Number of reference standards: Single test standard
* Limited verification: Full verification (not limited) Receives: Radiation: Taking a dental X-ray |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Sensitivity (also known as recall or true positive rate) is the proportion of actual positive cases that are correctly predicted as positive. It evaluates the performance of an AI algorithm. Formally it can be calculated with the following equation:
Sensitivity = TP / (TP+FN)
True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic
False Negative (FN) - a test result which wrongly indicates that a particular condition or characteristic is absent
Measured: Up to 6 weeks
Specificity
Specificity (also known as true negative rate) - is the proportion of actual negative cases that are correctly predicted as negative. It evaluates the performance of an AI algorithm. Formally it can be calculated by the equation below:
Specificity = TN / (TN + FP)
True negative (TN) - a test result that correctly indicates the absence of a condition or characteristic
False positive (FP) - a test result which wrongly indicates that a particular condition or characteristic is present
Measured: Up to 6 weeks
Precision of the AI algorithm
Precision is an evaluation metric used to assess the performance of machine learning algorithm for AI. It measures how accurate the algorithm is. We will use the number of true positives (TP) and false positives (FP) to calculate precision using the following formula:
Precision = TP / (TP + FP)
True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic
False positive (FP) - a test result that wrongly indicates that a particular condition or characteristic is present
Measured: Up to 6 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Feb 2024
- Study start1 Jan 2024
- Primary completion1 May 2024
- Study completion1 Nov 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Oral HealthStudies 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 1,025 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital of the Ministry of Interior, Kielce, Poland
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Maciej Sikora, Study chair, Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06258798
- Sponsor's protocol ID
- CT/2023/1
- Status last verified
- Mar 2025
- First submitted
- 25 Jan 2024
- Last updated
- 1 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Turosz N, Checinska K, Checinski M, Lubecka K, Blizniak F, Sikora M. Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study. Pediatr Rep. 2024 Sep 11;16(3):794-805. doi: 10.3390/pediatric16030067.
PubMed 39311330
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of the Dental X-ray Analysis Performed by an Artificial Intelligence Algorithm and the Analysis Performed by Dentists
- ClinicalTrials.gov ID
- NCT06258798
- Acronym / sponsor ID
- CT/2023/1
- Conditions
- Oral Health, Dental Caries, Periapical Diseases, Tooth Loss
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,025 participants (actual)
- Lead sponsor
- Hospital of the Ministry of Interior, Kielce, PolandUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 1 May 2024 (actual)
- Study completion
- 1 Nov 2024 (actual)
- First posted
- 14 Feb 2024 (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.
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, taking into account the location.
Detailed description
This cross-sectional study aims to perform a population-based assessment of the incidence of decay, dental fillings, root canal fillings, endodontic lesions, implants, implant and dental abutment crowns, pontic crowns, and missing teeth, considering the location. Patients with indications for dental X-ray confirmed by a written referral and with permanent dentition will participate in the study. Then, the X-rays will be analyzed by the dentists and the AI-based software after the data has been anonymized. The results will be compared to determine the AI algorithm's sensitivity, specificity, and precision.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| One group of patients (double gate) | – | Radiation: Taking a dental X-rayStudy design: * Direction of data collection: retrospective * Number of gates (sets of eligibility criteria): double gate (AI, human) * Participant sampling method: Consecutive * Method of allocating participants to index tests: Each participant received all index tests * Number of reference standards: Single test standard * Limited verification: Full verification (not limited) |
Who may qualify
- Ages
- 11 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients included in the study were admitted to the radiology department in Kielce, a city in southern Poland with around 200.000 inhabitants.
Key inclusion criteria (2 listed)
- Indications for dental X-ray confirmed by a written referral from the dentist or physician (both screening tests and tests performed for treatment purposes were allowed)
- Permanent dentition (after exfoliation is completed)
Key exclusion criteria (1 listed)
- Patients with mixed dentition (exfoliation has not finished)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Taking a dental X-rayDental X-rays taken in patients with indications confirmed by a written referral. | Radiation | One group of patients (double gate) |
Primary outcome
Sensitivity
Time frame: Up to 6 weeks
Sensitivity (also known as recall or true positive rate) is the proportion of actual positive cases that are correctly predicted as positive. It evaluates the performance of an AI algorithm. Formally it can be calculated with the following equation: Sensitivity = TP / (TP+FN) True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic False Negative (FN) - a test result which wrongly indicates that a particular condition or characteristic is absent
Specificity
Time frame: Up to 6 weeks
Specificity (also known as true negative rate) - is the proportion of actual negative cases that are correctly predicted as negative. It evaluates the performance of an AI algorithm. Formally it can be calculated by the equation below: Specificity = TN / (TN + FP) True negative (TN) - a test result that correctly indicates the absence of a condition or characteristic False positive (FP) - a test result which wrongly indicates that a particular condition or characteristic is present
Precision of the AI algorithm
Time frame: Up to 6 weeks
Precision is an evaluation metric used to assess the performance of machine learning algorithm for AI. It measures how accurate the algorithm is. We will use the number of true positives (TP) and false positives (FP) to calculate precision using the following formula: Precision = TP / (TP + FP) True positive (TP) - a test result that correctly indicates the presence of a condition or characteristic False positive (FP) - a test result that wrongly indicates that a particular condition or characteristic is present
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2025.
- Derived from this study: PubMed 39311330Turosz N, Checinska K, Checinski M, Lubecka K, Blizniak F, Sikora M. Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study. Pediatr Rep. 2024 Sep 11;16(3):794-805. doi: 10.3390/pediatric16030067.
- 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. This study is not recruiting at the moment.
Maciej Sikora study chair
Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland
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 Oral Health and Dental Caries, run by Hospital of the Ministry of Interior, Kielce, Poland at 1 site in Poland.
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 2 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: finished, as of the sponsor's last update on 1 Apr 2025. 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 Poland. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: Up to 6 weeks). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06258798, which also lists 1 linked publication.
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 1 Apr 2025.