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Artificial IntelligenceNCT05340140

The Accuracy of Detection of Artificial Intelligence Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images

An observational study of Artificial Intelligence, run by Cairo University at 1 site in Egypt.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial summary

CAD systems are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter, aiming at improving accuracy and reducing time for analysis. Read more about this study

Who is it for?
Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for
Artificial Intelligence
What is being tested?
deep learning model
How many people
50 participants
Where
1 siteEgypt
Study dates
May 2022to Oct 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 18 years and older
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

Study population: CBCT scans showing maxillary first molars with resolution not more than 0.1 mm voxel size.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 and from multiple private radiology centres using the same machine brand with the same parameters. CBCT scans of Egyptian patients who have already been subjected to CBCT examination as part of their dental diagnosis and/or treatment planning from January 2020 to December 2022 will be included according to the proposed eligibility criteria.

Who may be able to join

  • • CBCT scans showing erupted maxillary 1st molar.
  • Vovel size not exceeding 0.1mm.
  • Maxillary molars showing complete root formation.
  • Carious or Non-carious tooth.

Who cannot join

  • • Maxillary first molars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries.
  • CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.

Trial locations and contacts

1 site in 1 country.

Sally Mansour, Masters contact

+201019932383

sally.mansour@dentistry.cu.edu.eg

Ahmed MFM Magdy, MCS contact

+201019932383

ahmed_magdy@dentistry.cu.edu.eg

Egypt1 site, 1 recruiting

  • Faculty of dentistry cairo university

    Cairo, 12611

    Faculty ODC university, 01066365552, sally.mansour@dentistry.cu.edu.eg

    Recruiting

Other trials in Egypt

About this study

Official title: The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study

CAD systems are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter, 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 (ie, the capability of artificial intelligence [AI]) to best assist clinicians.

Read the detailed description

Countless studies and discussions have been based on the existence of a second canal in the mesiobuccal (MB) root of the maxillary molars , since it is strongly believed that one of the foremost reasons for endodontic failure in maxillary first molars is the difficulty of detecting and treating those second mesiobuccal (MB2) canals .The literature reveals that although MB2 canals of maxillary first molars have been found in more than 70% of in vitro studies , they were detected clinically in less than 40% of cases . Cone beam computed tomography (CBCT) is an imaging modality in the field of endodontics that has several advantages, including the ability to perform three-dimensional (3D) imaging of root canal systems with lower radiation doses, higher resolution, and no superimposition . Researchers have evaluated the efficiency of CBCT when it comes to identifying MB2 canals, and CBCT has been suggested to be a reliable method for the detection of these canals. However, in clinically relevant situations, such a smaller lesions on root-filled teeth, CBCT accuracy is greatly reduced (sensitivity 0.63, specificity 0.69) . Moreover, clinician dependent interpretation of CBCT imaging still suffers from low inter- and intra-observer agreement.

Computer-aided detection and diagnosis (CAD) has been widely applied to biomedical image analysis outside of dentistry .

Being tested
deep learning model

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • deep learning modelDiagnostic test

    Also called artificial intelligence tool

    deep learning model developed by computer science expert and based on convolution neural network , and trained by our datasets.

Study groups in NCT05340140
Study groupRoleWhat they receive
CBCT Images of Maxillary 1st molarsNot described
Receives: Diagnostic Test: deep learning model

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. accuracy of detection of MB2

    detection of MB2 on CBCT images of maxillary first molars using deep learning model

    Measured: baseline

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted21 Apr 2022
  2. Study startMay 2022 (estimated)
  3. Primary completionSep 2023 (estimated)
  4. Study completionOct 2023 (estimated)

Resources

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
Other
Enrolment
50 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05340140
Sponsor's protocol ID
CBCT AI 7-1-1
Status last verified
Apr 2022
First submitted
15 Apr 2022
Last updated
21 Apr 2022
Results posted
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Undecided

Publications

  • Blattner TC, George N, Lee CC, Kumar V, Yelton CD. Efficacy of cone-beam computed tomography as a modality to accurately identify the presence of second mesiobuccal canals in maxillary first and second molars: a pilot study. J Endod. 2010 May;36(5):867-70. doi: 10.1016/j.joen.2009.12.023. Epub 2010 Feb 21.

    PubMed 20416435

  • Kulild JC, Peters DD. Incidence and configuration of canal systems in the mesiobuccal root of maxillary first and second molars. J Endod. 1990 Jul;16(7):311-7. doi: 10.1016/s0099-2399(06)81940-0.

    PubMed 2081944

  • Alacam T, Tinaz AC, Genc O, Kayaoglu G. Second mesiobuccal canal detection in maxillary first molars using microscopy and ultrasonics. Aust Endod J. 2008 Dec;34(3):106-9. doi: 10.1111/j.1747-4477.2007.00090.x.

    PubMed 19032644

  • Gorduysus MO, Gorduysus M, Friedman S. Operating microscope improves negotiation of second mesiobuccal canals in maxillary molars. J Endod. 2001 Nov;27(11):683-6. doi: 10.1097/00004770-200111000-00008.

    PubMed 11716081

  • Weine FS, Hayami S, Hata G, Toda T. Canal configuration of the mesiobuccal root of the maxillary first molar of a Japanese sub-population. Int Endod J. 1999 Mar;32(2):79-87. doi: 10.1046/j.1365-2591.1999.00186.x.

    PubMed 10371900

  • Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019 Jul;45(7):917-922.e5. doi: 10.1016/j.joen.2019.03.016. Epub 2019 Jun 1.

    PubMed 31160078

  • Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019 Mar;48(3):20180218. doi: 10.1259/dmfr.20180218. Epub 2018 Nov 9.

    PubMed 30379570

  • Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020 May;53(5):680-689. doi: 10.1111/iej.13265. Epub 2020 Feb 3.

    PubMed 31922612

  • Mansour S, Anter E, Mohamed AK, Dahaba MM, Mousa A. Two step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence. BMC Oral Health. 2025 Sep 8;25(1):1404. doi: 10.1186/s12903-025-06796-4.

    PubMed 40926256

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 2 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 21 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 Sally Mansour, Masters (+201019932383, sally.mansour@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 detection of MB2 (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 NCT05340140, which also lists 9 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 21 Apr 2022.

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