Artificial IntelligenceNCT05350228
Accuracy of Artificial Intelligence in Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT
An observational study of Artificial Intelligence, run by Cairo University at 1 site in Egypt.
Trial summary
Convolutional neural network (CNN) are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter10, aiming at improving accuracy and reducing time for analysis. Read more about this study
- Who is it for?
- Ages 25 to 65 years
- Conditions the study is for
- Artificial Intelligence
- What is being tested?
- CNN based model
- How many people
- 50 participants
- Where
- 1 siteEgypt
- Study dates
- May 2022to Dec 2023
- 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 25 to 65 years
- All sexes
- Adult, Older adult
Study population: The CBCT data of this study will be obtained from the CBCT data base available at the department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, Egypt. CBCT scans of patients who have already been subjected to CBCT examination as part of their dental diagnosis and/or treatment planning will be included according to the proposed eligibility criteria.
Who may be able to join
- • CBCT Scans showing Mandibular third molar of patients aging from 25 to 65 years old
- The FOV should clearly show the third molar completely with its roots and the IAN.
- Voxel size of 0.2mm.
- Mandibular third molars. Absence of artifacts, dental implants in the adjacent teeth.
Who cannot join
- • CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment.
Trial locations and contacts
1 site in 1 country.
Egypt1 site, 1 recruiting
- Recruiting
Faculty of dentistry cairo university
Cairo, 12611
Faculty university, 01066365552, sally.mansour@dentistry.cu.edu.eg
About this study
Official title: Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study.
Convolutional neural network (CNN) are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter10, aiming at improving accuracy and reducing time for analysis. With the rapid growth of Deep Learning (DL) algorithms in image-based applications, CAD systems can now be trained by DL to provide more advanced capability (i.e., the capability of artificial intelligence [AI]) to best assist clinicians).
Read the detailed description
The mandibular third molar extraction, considered one of the most common surgeries in oral and maxillofacial field, it can be associated with several postoperative complications, like pain, bleeding, swelling, and inferior alveolar nerve (IAN) injury or complete damage, impairing the quality of life of the affected patients. The incidence of temporary IAN injury caused by mandibular third molar extraction was 0.4-8.4%, while the incidence of permanent injury is less than 1% [1, 2]. However, due to the high occurrence of impacted mandibular third molar, a large number of patients suffer from IAN injury caused by impacted mandibular third molar extraction [3]. The most significant risk factor of IAN injury caused by mandibular third molar extraction is the proximity of the root of the mandibular third molar to the mandibular canal [1, 2, 4, 5]. So, comprehensive preoperative analysis and evaluation of the anatomical structures are essential before impacted mandibular third molar extraction to decrease the IAN injury risk.
The panoramic radiography is not that much accurate in displaying the relation between impacted mandibular third molar extraction and IAN due to the superimposition and inherent limitations. The accuracy of predicting the probability the (IAN) injury during the impacted mandibular third molar extraction using panoramic radiographs were controversial [6].
Cone beam computed tomography (CBCT), A (3D) imaging modality, provides accurate 3D information with decreased radiation dose than medical CT [7]. It was demonstrated that CBCT was a better and accurate radiographic method than panoramic radiography for evaluating the relationship between mandibular third molar and (IAN) [6, 8]. So that, CBCT has been considered as the modality of choice for preoperative assessment of complicated mandibular third molar extraction [9].
Deep learning, one of artificial intelligence subsets, had a rapid progression and has a significant role in medical fields. One of the deep learning models, guided learning of the convolutional neural network (CNN) is recently investigated, which has been proven to surpass human judgmental level in many medical imaging fields [12, 13]. After CNN was introduced to the maxillofacial field, it was used for the assessment, detection, categorization, and segmentation of the surrounding anatomical structures [14-18 Recently, deep learning based on CNN models has been used for the impacted mandibular third molar and mandibular canal detection and segmentation on panoramic radiographs and CBCT [15, 18, 30], the classification and staging of development [31, 32], and the approximation measurements of the impacted mandibular third molar on panoramic radiographs [33]. Fukuda et al. compared 3 CNNs for classification of the impacted mandibular third molar and mandibular canal relation with panoramic radiographs [34]. Yoo et al. proposed a CNN-based approach to assess the stalemate of the impacted mandibular third molar extraction using panoramic radiographs [35]. So, as mentioned before, panoramic radiography can't accurately describe the anatomical structures due to the superimposition that happens in the (2D) imaging modalities. Orhan et al. reported an AI application (Diagnocat, Inc.) based on CNN with high precision in detecting the M3 and assessment of the number of roots related to adjacent anatomical structures
- Conditions
- Artificial Intelligence
- Being tested
- CNN based model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CNN based modelDiagnostic test
Also called artificial intelligence tool
It is an automatic detector model based on convolution neural network created by computer science expert
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the automatic evaluation of the relationship between mandibular third molar and the mandibular canal.
Accuracy of the deep learning model in automatic evaluation of mandibular third molar teeth and mandibular canal relationship.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Apr 2022
- Study startMay 2022 (estimated)
- Primary completionDec 2023 (estimated)
- Study completionDec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)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 Cairo UniversityThe 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
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sally Mansour, Cairo University
- Investigators
- Enas Anter, Study director, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05350228
- Sponsor's protocol ID
- ORAD AI 1-1
- Status last verified
- Apr 2022
- First submitted
- 22 Apr 2022
- Last updated
- 28 Apr 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Leung YY, Cheung LK. Risk factors of neurosensory deficits in lower third molar surgery: an literature review of prospective studies. Int J Oral Maxillofac Surg. 2011 Jan;40(1):1-10. doi: 10.1016/j.ijom.2010.09.005. Epub 2010 Oct 28.
PubMed 21035310Gulicher D, Gerlach KL. Sensory impairment of the lingual and inferior alveolar nerves following removal of impacted mandibular third molars. Int J Oral Maxillofac Surg. 2001 Aug;30(4):306-12. doi: 10.1054/ijom.2001.0057.
PubMed 11518353Ghaeminia H, Meijer GJ, Soehardi A, Borstlap WA, Mulder J, Berge SJ. Position of the impacted third molar in relation to the mandibular canal. Diagnostic accuracy of cone beam computed tomography compared with panoramic radiography. Int J Oral Maxillofac Surg. 2009 Sep;38(9):964-71. doi: 10.1016/j.ijom.2009.06.007. Epub 2009 Jul 28.
PubMed 19640685Tay AB, Go WS. Effect of exposed inferior alveolar neurovascular bundle during surgical removal of impacted lower third molars. J Oral Maxillofac Surg. 2004 May;62(5):592-600. doi: 10.1016/j.joms.2003.08.033.
PubMed 15122566Kim JW, Cha IH, Kim SJ, Kim MR. Which risk factors are associated with neurosensory deficits of inferior alveolar nerve after mandibular third molar extraction? J Oral Maxillofac Surg. 2012 Nov;70(11):2508-14. doi: 10.1016/j.joms.2012.06.004. Epub 2012 Aug 15.
PubMed 22901857Kwak GH, Kwak EJ, Song JM, Park HR, Jung YH, Cho BH, Hui P, Hwang JJ. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020 Mar 31;10(1):5711. doi: 10.1038/s41598-020-62586-8.
PubMed 32235882
Study at a glance
From the registry record, last updated by the sponsor on 28 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study.
- ClinicalTrials.gov ID
- NCT05350228
- Acronym / sponsor ID
- ORAD AI 1-1
- Conditions
- Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- May 2022 (estimated)
- Primary completion
- Dec 2023 (estimated)
- Study completion
- Dec 2023 (estimated)
- First posted
- 28 Apr 2022 (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.
Convolutional neural network (CNN) are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter10, aiming at improving accuracy and reducing time for analysis. With the rapid growth of Deep Learning (DL) algorithms in image-based applications, CAD systems can now be trained by DL to provide more advanced capability (i.e., the capability of artificial intelligence [AI]) to best assist clinicians).
Detailed description
The mandibular third molar extraction, considered one of the most common surgeries in oral and maxillofacial field, it can be associated with several postoperative complications, like pain, bleeding, swelling, and inferior alveolar nerve (IAN) injury or complete damage, impairing the quality of life of the affected patients. The incidence of temporary IAN injury caused by mandibular third molar extraction was 0.4-8.4%, while the incidence of permanent injury is less than 1% [1, 2]. However, due to the high occurrence of impacted mandibular third molar, a large number of patients suffer from IAN injury caused by impacted mandibular third molar extraction [3]. The most significant risk factor of IAN injury caused by mandibular third molar extraction is the proximity of the root of the mandibular third molar to the mandibular canal [1, 2, 4, 5]. So, comprehensive preoperative analysis and evaluation of the anatomical structures are essential before impacted mandibular third molar extraction to decrease the IAN injury risk.
The panoramic radiography is not that much accurate in displaying the relation between impacted mandibular third molar extraction and IAN due to the superimposition and inherent limitations. The accuracy of predicting the probability the (IAN) injury during the impacted mandibular third molar extraction using panoramic radiographs were controversial [6].
Cone beam computed tomography (CBCT), A (3D) imaging modality, provides accurate 3D information with decreased radiation dose than medical CT [7]. It was demonstrated that CBCT was a better and accurate radiographic method than panoramic radiography for evaluating the relationship between mandibular third molar and (IAN) [6, 8]. So that, CBCT has been considered as the modality of choice for preoperative assessment of complicated mandibular third molar extraction [9].
Deep learning, one of artificial intelligence subsets, had a rapid progression and has a significant role in medical fields. One of the deep learning models, guided learning of the convolutional neural network (CNN) is recently investigated, which has been proven to surpass human judgmental level in many medical imaging fields [12, 13]. After CNN was introduced to the maxillofacial field, it was used for the assessment, detection, categorization, and segmentation of the surrounding anatomical structures [14-18 Recently, deep learning based on CNN models has been used for the impacted mandibular third molar and mandibular canal detection and segmentation on panoramic radiographs and CBCT [15, 18, 30], the classification and staging of development [31, 32], and the approximation measurements of the impacted mandibular third molar on panoramic radiographs [33]. Fukuda et al. compared 3 CNNs for classification of the impacted mandibular third molar and mandibular canal relation with panoramic radiographs [34]. Yoo et al. proposed a CNN-based approach to assess the stalemate of the impacted mandibular third molar extraction using panoramic radiographs [35]. So, as mentioned before, panoramic radiography can't accurately describe the anatomical structures due to the superimposition that happens in the (2D) imaging modalities. Orhan et al. reported an AI application (Diagnocat, Inc.) based on CNN with high precision in detecting the M3 and assessment of the number of roots related to adjacent anatomical structures
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 25 to 65 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The CBCT data of this study will be obtained from the CBCT data base available at the department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, Egypt. CBCT scans of patients who have already been subjected to CBCT examination as part of their dental diagnosis and/or treatment planning will be included according to the proposed eligibility criteria.
Key inclusion criteria (4 listed)
- • CBCT Scans showing Mandibular third molar of patients aging from 25 to 65 years old
Key exclusion criteria (1 listed)
- • CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment.
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 |
|---|---|---|
| CNN based modelAlso: artificial intelligence toolIt is an automatic detector model based on convolution neural network created by computer science expert | Diagnostic test | – |
Primary outcome
Accuracy of the automatic evaluation of the relationship between mandibular third molar and the mandibular canal.
Time frame: baseline
Accuracy of the deep learning model in automatic evaluation of mandibular third molar teeth and mandibular canal relationship.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Apr 2022.
- Background: PubMed 21035310Leung YY, Cheung LK. Risk factors of neurosensory deficits in lower third molar surgery: an literature review of prospective studies. Int J Oral Maxillofac Surg. 2011 Jan;40(1):1-10. doi: 10.1016/j.ijom.2010.09.005. Epub 2010 Oct 28.
- Background: PubMed 11518353Gulicher D, Gerlach KL. Sensory impairment of the lingual and inferior alveolar nerves following removal of impacted mandibular third molars. Int J Oral Maxillofac Surg. 2001 Aug;30(4):306-12. doi: 10.1054/ijom.2001.0057.
- Background: PubMed 19640685Ghaeminia H, Meijer GJ, Soehardi A, Borstlap WA, Mulder J, Berge SJ. Position of the impacted third molar in relation to the mandibular canal. Diagnostic accuracy of cone beam computed tomography compared with panoramic radiography. Int J Oral Maxillofac Surg. 2009 Sep;38(9):964-71. doi: 10.1016/j.ijom.2009.06.007. Epub 2009 Jul 28.
- Background: PubMed 15122566Tay AB, Go WS. Effect of exposed inferior alveolar neurovascular bundle during surgical removal of impacted lower third molars. J Oral Maxillofac Surg. 2004 May;62(5):592-600. doi: 10.1016/j.joms.2003.08.033.
- Background: PubMed 22901857Kim JW, Cha IH, Kim SJ, Kim MR. Which risk factors are associated with neurosensory deficits of inferior alveolar nerve after mandibular third molar extraction? J Oral Maxillofac Surg. 2012 Nov;70(11):2508-14. doi: 10.1016/j.joms.2012.06.004. Epub 2012 Aug 15.
- Background: PubMed 32235882Kwak GH, Kwak EJ, Song JM, Park HR, Jung YH, Cho BH, Hui P, Hwang JJ. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020 Mar 31;10(1):5711. doi: 10.1038/s41598-020-62586-8.
- 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.
Enas Anter study director
Cairo University
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, run by Cairo University at 1 site in Egypt.
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 4 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: status not confirmed, as of the sponsor's last update on 28 Apr 2022. 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 Egypt. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Ahmed Salama, Msc (+201019932383, ahmed_magdy@dentistry.cu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the automatic evaluation of the relationship between mandibular third molar and the mandibular canal. (time frame: baseline). 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 NCT05350228, which also lists 6 linked publications.
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 28 Apr 2022.