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

NCT ID: NCT05340140

Last Updated: 2022-04-21

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 50 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-05-31
• Study Completion Date: 2023-10-31

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.

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 .

Conditions

Artificial Intelligence

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

CBCT Images of Maxillary 1st molars

deep learning model

Intervention Type: DIAGNOSTIC_TEST

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

Interventions

deep learning model

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

Intervention Type: DIAGNOSTIC_TEST

Other Intervention Names

artificial intelligence tool

Eligibility Criteria

Inclusion Criteria

• • CBCT scans showing erupted maxillary 1st molar.

• Vovel size not exceeding 0.1mm.
• Maxillary molars showing complete root formation.
• Carious or Non-carious tooth.

Exclusion Criteria

• • 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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Cairo University

OTHER

Sponsor Role: lead

Responsible Party

Arwa Mousa

lecturer of oral and maxillofacial radiology, faculty of dentistry

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Enas Anter, Ph.D

Role: STUDY_DIRECTOR

Cairo University

Locations

Faculty of dentistry cairo university

Cairo, , Egypt

Site Status: RECRUITING

Countries

Egypt

Central Contacts

Sally Mansour, Masters

Role: CONTACT

sally.mansour@dentistry.cu.edu.eg

+201019932383

Ahmed MFM Magdy, MCS

Role: CONTACT

ahmed_magdy@dentistry.cu.edu.eg

+201019932383

Facility Contacts

Faculty ODC university

Role: primary

sally.mansour@dentistry.cu.edu.eg

01066365552

References

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.

Reference Type: BACKGROUND

PMID: 20416435 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 2081944 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 19032644 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 11716081 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 10371900 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 31160078 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30379570 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 31922612 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 40926256 (View on PubMed)

Other Identifiers

CBCT AI 7-1-1

Identifier Type: -

Identifier Source: org_study_id