Artificial IntelligenceNCT05912361
Impact of Training Dental Students for an AI-Based Platform
A study testing receiving a one hour theoretical and hands on training session before using an AI-based platform in people with Artificial Intelligence and Education, run by University of Copenhagen at 1 site in Denmark.
Trial summary
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials in finding radiographic features and treatment planning in the field of cariology and endodontics . Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Education
- What is being tested?
- receiving a one hour theoretical and hands on training session before using an AI-based platform
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 20 participants
- Where
- 1 siteDenmark
- Study dates
- 20 Aug 2023to 1 Jan 2024
- Phase
- Interventional
- 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 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- perhaps 4th year and 5th year dental students at the university of Copenhagen who are willing to participate voluntarily and have signed the consent letter.
- Limited or no previous knowledge and experience about AI
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
Denmark1 site
University of Copenhagen Department of Odontology Cariology and Endodontics Section for Clinical Oral Microbiology
Copenhagen, 2200
About this study
Official title: The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation: A Randomized Controlled Trial
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials in finding radiographic features and treatment planning in the field of cariology and endodontics . A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographical features such as carious lesions, periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, current literature lacks sufficient research on the effect of sufficient training of dental practitioners for using AI-based platforms. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for pulp exposure prediction with and without sufficient preprocedural training. The hypothesis is that participants performance at group with sufficient training is similar to the group without sufficient training.
- Conditions
- Artificial Intelligence, Education
- Being tested
- receiving a one hour theoretical and hands on training session before using an AI-based platform
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
receiving a one hour theoretical and hands on training session before using an AI-based platformBehavioural
The students at the experimental group will receive a one-hour hands-on training session before logging in to the online platform. The session will be presented by a dentist with AI experience and this session will present basic aspects of AI in radiology, deep learning (DL) applications for cariology and endodontics, as well as basics of excavation therapy and pulp exposure. the theoretical part will be followed by a hands on session on which each participant will check 11 cases of teeth with deep caries and will find the closest line between caries and pulp. their performance will be supervised by the training session presenter and the correct line will be shown them in case of making wrong line.
| Study group | Role | What they receive |
|---|---|---|
| Students using AI-platform for assessing the risk of pulp exposure receiving a training session | Experimental | Students will go through a one-hour hands-on training session before taking the test at the online platform. The session includes a theoretical session related to basic aspects of AI in radiology, CNN (Convolutional Neural Network) applications for cariology and endodontics, as well as basics of excavation therapy and pulp exposure. the theoretical part will be followed by a hands on session on which participants check 11 cases of teeth with deep caries and will find the closest line between caries and pulp.
Then, they will receive access to log in to the website on which pretreatment x-rays of cases undergoing caries excavation therapy is uploaded. The performance of students on will be assessed. Receives: Behavioral: receiving a one hour theoretical and hands on training session before using an AI-based platform |
| Students using AI-platform for assessing the risk of pulp exposure without any training session | No intervention | Students will not receive any training before starting the experiment. Only a 5-minute video will be played as the guide for answering the questions in the website. Then, they will receive access to log in to the website on which pretreatment x-rays of cases undergoing caries excavation therapy is uploaded. The performance of students on will be assessed. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy
The accuracy of students at both group (with and without training session) will be measured and compared together. The accuracy measurement for each student will be calculated by the number of correct predictions of pulp exposure occurrence divided by the total predictions.
Measured: 30 days
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
The sensitivity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual pulp exposure cases that got predicted as pulp exposure (true positive).
Measured: 30 days
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their specificity
The specificity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual 'no pulp exposure' cases correctly predicted as cases without pulp exposure (true negative).
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2023
- Study start20 Aug 2023
- Primary completion20 Dec 2023
- Study completion1 Jan 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 Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by University of CopenhagenThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 20 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Copenhagen
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05912361
- Sponsor's protocol ID
- 504-0342/22-5000
- Status last verified
- Jan 2024
- First submitted
- 24 May 2023
- Last updated
- 8 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ramezanzade S, Dascalu TL, Bakhshandeh A, Uribe SE, Ibragimov B, Bjorndal L. The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial. Int Endod J. 2026 Jun;59(6):1248-1256. doi: 10.1111/iej.70046. Epub 2025 Oct 10.
PubMed 41074395
Study at a glance
From the registry record, last updated by the sponsor on 8 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT05912361
- Acronym / sponsor ID
- 504-0342/22-5000
- Conditions
- Artificial Intelligence, Education
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 20 participants (actual)
- Lead sponsor
- University of CopenhagenUniversity, hospital or other
- Study start
- 20 Aug 2023 (actual)
- Primary completion
- 20 Dec 2023 (actual)
- Study completion
- 1 Jan 2024 (actual)
- First posted
- 22 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.
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials in finding radiographic features and treatment planning in the field of cariology and endodontics . A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographical features such as carious lesions, periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, current literature lacks sufficient research on the effect of sufficient training of dental practitioners for using AI-based platforms. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for pulp exposure prediction with and without sufficient preprocedural training. The hypothesis is that participants performance at group with sufficient training is similar to the group without sufficient training.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Students using AI-platform for assessing the risk of pulp exposure receiving a training session | Experimental | Behavioral: receiving a one hour theoretical and hands on training session before using an AI-based platformStudents will go through a one-hour hands-on training session before taking the test at the online platform. The session includes a theoretical session related to basic aspects of AI in radiology, CNN (Convolutional Neural Network) applications for cariology and endodontics, as well as basics of excavation therapy and pulp exposure. the theoretical part will be followed by a hands on session on which participants check 11 cases of teeth with deep caries and will find the closest line between caries and pulp. Then, they will receive access to log in to the website on which pretreatment x-rays of cases undergoing caries excavation therapy is uploaded. The performance of students on will be assessed. |
| Students using AI-platform for assessing the risk of pulp exposure without any training session | No intervention | –Students will not receive any training before starting the experiment. Only a 5-minute video will be played as the guide for answering the questions in the website. Then, they will receive access to log in to the website on which pretreatment x-rays of cases undergoing caries excavation therapy is uploaded. The performance of students on will be assessed. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- perhaps 4th year and 5th year dental students at the university of Copenhagen who are willing to participate voluntarily and have signed the consent letter.
- Limited or no previous knowledge and experience about AI
Key exclusion criteria (1 listed)
- None
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 |
|---|---|---|
| receiving a one hour theoretical and hands on training session before using an AI-based platformThe students at the experimental group will receive a one-hour hands-on training session before logging in to the online platform. The session will be presented by a dentist with AI experience and this session will present basic aspects of AI in radiology, deep learning (DL) applications for cariology and endodontics, as well as basics of excavation therapy and pulp exposure. the theoretical part will be followed by a hands on session on which each participant will check 11 cases of teeth with deep caries and will find the closest line between caries and pulp. their performance will be supervised by the training session presenter and the correct line will be shown them in case of making wrong line. | Behavioural | Students using AI-platform for assessing the risk of pulp exposure receiving a training session |
Primary outcome
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy
Time frame: 30 days
The accuracy of students at both group (with and without training session) will be measured and compared together. The accuracy measurement for each student will be calculated by the number of correct predictions of pulp exposure occurrence divided by the total predictions.
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
Time frame: 30 days
The sensitivity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual pulp exposure cases that got predicted as pulp exposure (true positive).
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their specificity
Time frame: 30 days
The specificity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual 'no pulp exposure' cases correctly predicted as cases without pulp exposure (true negative).
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 8 Jan 2024.
- Derived from this study: PubMed 41074395Ramezanzade S, Dascalu TL, Bakhshandeh A, Uribe SE, Ibragimov B, Bjorndal L. The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial. Int Endod J. 2026 Jun;59(6):1248-1256. doi: 10.1111/iej.70046. Epub 2025 Oct 10.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing receiving a one hour theoretical and hands on training session before using an AI-based platform in people with Artificial Intelligence and Education, run by University of Copenhagen at 1 site in Denmark.
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 8 Jan 2024. 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 Denmark. 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?
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy (time frame: 30 days). 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 NCT05912361, 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 8 Jan 2024.