NCT05350228

Brief 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. 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).

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
50

participants targeted

Target at P25-P50 for all trials

Timeline
Completed

Started May 2022

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

April 22, 2022

Completed
6 days until next milestone

First Posted

Study publicly available on registry

April 28, 2022

Completed
3 days until next milestone

Study Start

First participant enrolled

May 1, 2022

Completed
1.6 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 1, 2023

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 1, 2023

Completed
Last Updated

April 28, 2022

Status Verified

April 1, 2022

Enrollment Period

1.6 years

First QC Date

April 22, 2022

Last Update Submit

April 22, 2022

Conditions

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

  • 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.

    baseline

Interventions

CNN based modelDIAGNOSTIC_TEST

It is an automatic detector model based on convolution neural network created by computer science expert

Also known as: artificial intelligence tool

Eligibility Criteria

Age25 Years - 65 Years
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
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.

You may qualify if:

  • 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.

You may not qualify if:

  • CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Faculty of dentistry cairo university

Cairo, 12611, Egypt

RECRUITING

Related Publications (6)

  • 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.

    PMID: 21035310BACKGROUND

  • Gulicher 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.

    PMID: 11518353BACKGROUND

  • Ghaeminia 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.

    PMID: 19640685BACKGROUND

  • Tay 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.

    PMID: 15122566BACKGROUND

  • Kim 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.

    PMID: 22901857BACKGROUND

  • Kwak 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.

    PMID: 32235882BACKGROUND

Study Officials

  • Enas Anter

    Cairo University

    STUDY DIRECTOR

Central Study Contacts

Ahmed Salama, Msc

CONTACT

+201019932383ahmed_magdy@dentistry.cu.edu.eg

Sally Mansour, Msc

CONTACT

+201066365552sally.mansour@dentistry.cu.edu.eg

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
CROSS SECTIONAL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
assistant lecturer of oral and maxillofacial radiology, faculty of dentistry

Study Record Dates

First Submitted

April 22, 2022

First Posted

April 28, 2022

Study Start

May 1, 2022

Primary Completion

December 1, 2023

Study Completion

December 1, 2023

Last Updated

April 28, 2022

Record last verified: 2022-04

Locations

EG(1)