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

NCT05340140 | Unknown

• 1 other identifier: CBCT AI 7-1-1
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• accuracy of detection of MB2

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

  baseline

Study Arms (1)

CBCT Images of Maxillary 1st molars

Diagnostic Test: deep learning model

Interventions

deep learning model DIAGNOSTIC_TEST

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

Also known as: artificial intelligence tool

CBCT Images of Maxillary 1st molars

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Cairo University: lead

Study Sites (1)

Faculty of dentistry cairo university

Cairo, 12611, Egypt

RECRUITING

Related Publications (9)

• 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.

  PMID: 20416435 BACKGROUND

• 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.

  PMID: 2081944 BACKGROUND

• 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.

  PMID: 19032644 BACKGROUND

• 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.

  PMID: 11716081 BACKGROUND

• 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.

  PMID: 10371900 BACKGROUND

• 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.

  PMID: 31160078 BACKGROUND

• 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.

  PMID: 30379570 BACKGROUND

• 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.

  PMID: 31922612 BACKGROUND

• 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.

  PMID: 40926256 DERIVED

Study Officials

• Enas Anter, Ph.D

  Cairo University

  STUDY DIRECTOR

Central Study Contacts

Sally Mansour, Masters

CONTACT

+201019932383; sally.mansour@dentistry.cu.edu.eg

Ahmed MFM Magdy, MCS

CONTACT

+201019932383; ahmed_magdy@dentistry.cu.edu.eg

Study Design

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

Study Record Dates

• First Submitted: April 15, 2022
• First Posted: April 21, 2022
• Study Start: May 1, 2022
• Primary Completion: September 1, 2023
• Study Completion: October 1, 2023
• Last Updated: April 21, 2022

Record last verified: 2022-04

Locations

EG (1)