Artificial IntelligenceNCT04309851
A Deep Learning Approach to Submerged Teeth Classification and Detection
An observational study of Artificial Intelligence and Submerging Tooth, run by Eskisehir Osmangazi University at 1 site in Turkey (Türkiye).
Trial summary
Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG). Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. Read more about this study
- Who is it for?
- Ages 5 to 12 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Submerging Tooth
- What is being tested?
- deep learning
- How many people
- 74 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jan 2019to 1 Mar 2020
- 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 5 to 12 years
- All sexes
- Healthy volunteers accepted
- Child
Study population: The data set included OPGs of 19000 children aged 5-12 years that were collected between January 2016 and December 2018.
Who may be able to join
None listed.
Who cannot join
- OPG images of poor quality (metal artifact, artifacts due to position errors during shooting, etc.) were excluded.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Deep Learning Approach to Submerged Deciduous Teeth Classification and Detection
Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG).
Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it to the expert level.
Read more
Results: The success rate of classification and identification of the system is high when evaluated according to the reference standard. The system was extremely accurate in performance comparison with observers.
Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an artificial intelligence application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.
Read the detailed description
Pre-processing, Training, and Classification The study was conducted with balanced data sets. The case and control data sets were randomly divided into two parts, the training group (27 case group/27 control group) and the test group (10 case group/10 control group) to prevent the use of the visuals in the training group for retesting. The testing data set was not seen by the system during the training phase.
All 2943-by-1435 pixel images in the data set were resized to 971 by 474 pixels prior to training. All OPG images used include the whole dentitions. The training and test data sets were used to estimate and generate weight factors for the optimal CNN algorithm. An arbitrary sequence was generated using open-source Python programming (Python 3.6.1, Python Software Foundation, Wilmington, DE, USA, https://www.python.org/) language and OpenCV, NumPy, Pandas, and Matplotlib libraries. In this study, Tensorflow for model development was used to classify submerged primary molars. InceptionV3 architecture was used as transfer learning, and the transfer values were saved in the cache. Then, fully connected layer and softmax classifiers were combined to form the final model layers. The training was carried out using 7000 steps with 16G RAM and a PC equipped with NVIDIA GeForce GTX 1050.
- Conditions
- Artificial Intelligence, Submerging Tooth
- Being tested
- deep learning
- Keywords
- artificial intelligence, infraocclusion, submerged tooth
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
deep learningDiagnostic test
the deep learning method is a field of study involving artificial neural networks and similar machine learning algorithms with many hidden layers.
What is being measured
The primary outcome is the main question the study is designed to answer.
Submerged Tooth Detection
The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Mar 2020
- Study start1 Jan 2019
- Primary completion1 Jan 2020
- Study completion1 Mar 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 74 participants (actual)
Sponsor and investigators
- Lead sponsor
- Eskisehir Osmangazi University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Seçil Çalışkan, Eskisehir Osmangazi University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04309851
- Sponsor's protocol ID
- E86412-49
- Status last verified
- Mar 2020
- First submitted
- 12 Mar 2020
- Last updated
- 16 Mar 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 16 Mar 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning Approach to Submerged Deciduous Teeth Classification and Detection
- ClinicalTrials.gov ID
- NCT04309851
- Acronym / sponsor ID
- E86412-49
- Conditions
- Artificial Intelligence, Submerging Tooth
- Keywords
- artificial intelligence, infraocclusion, submerged tooth
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 74 participants (actual)
- Lead sponsor
- Eskisehir Osmangazi UniversityUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 1 Jan 2020 (actual)
- Study completion
- 1 Mar 2020 (actual)
- First posted
- 16 Mar 2020 (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.
Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG).
Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it to the expert level.
Results: The success rate of classification and identification of the system is high when evaluated according to the reference standard. The system was extremely accurate in performance comparison with observers.
Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an artificial intelligence application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.
Detailed description
Pre-processing, Training, and Classification The study was conducted with balanced data sets. The case and control data sets were randomly divided into two parts, the training group (27 case group/27 control group) and the test group (10 case group/10 control group) to prevent the use of the visuals in the training group for retesting. The testing data set was not seen by the system during the training phase.
All 2943-by-1435 pixel images in the data set were resized to 971 by 474 pixels prior to training. All OPG images used include the whole dentitions. The training and test data sets were used to estimate and generate weight factors for the optimal CNN algorithm. An arbitrary sequence was generated using open-source Python programming (Python 3.6.1, Python Software Foundation, Wilmington, DE, USA, https://www.python.org/) language and OpenCV, NumPy, Pandas, and Matplotlib libraries. In this study, Tensorflow for model development was used to classify submerged primary molars. InceptionV3 architecture was used as transfer learning, and the transfer values were saved in the cache. Then, fully connected layer and softmax classifiers were combined to form the final model layers. The training was carried out using 7000 steps with 16G RAM and a PC equipped with NVIDIA GeForce GTX 1050.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 5 to 12 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- The data set included OPGs of 19000 children aged 5-12 years that were collected between January 2016 and December 2018.
Key inclusion criteria (0 listed)
Key exclusion criteria (1 listed)
- OPG images of poor quality (metal artifact, artifacts due to position errors during shooting, etc.) were excluded.
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 |
|---|---|---|
| deep learningthe deep learning method is a field of study involving artificial neural networks and similar machine learning algorithms with many hidden layers. | Diagnostic test | – |
Primary outcome
Submerged Tooth Detection
Time frame: 6 months
The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars.
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 16 Mar 2020.
- Individual participant dataSharing planned: Undecided
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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?
An observational study of Artificial Intelligence and Submerging Tooth, run by Eskisehir Osmangazi University at 1 site in Turkey (Türkiye).
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 0 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 16 Mar 2020. 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 Turkey (Türkiye). 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?
Submerged Tooth Detection (time frame: 6 months). 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 16 Mar 2020.