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

Artificial Intelligence Clinical Trial

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05340140
• Other study ID #: CBCT AI 7-1-1
• Status: Recruiting
• Start date: May 2022
• Est. completion date: October 2023

Study information

• Verified date: April 2022
• Source: Cairo University
• Contact: Sally Mansour, Masters
• Phone: +201019932383
• Email: sally.mansour[@]dentistry.cu.edu.eg
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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 .

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50
• Est. completion date: October 2023
• Est. primary completion date: September 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence

Intervention

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

Locations

Country	Name	City	State
Egypt	Faculty of dentistry cairo university	Cairo

Sponsors (1)

Lead Sponsor	Collaborator
Cairo University

Country where clinical trial is conducted

Egypt, 

References & Publications (8)

Alaçam T, Tinaz AC, Genç 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. — View Citation

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. — View Citation

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. — View Citation

Görduysus MO, Görduysus M, Friedman S. Operating microscope improves negotiation of second mesiobuccal canals in maxillary molars. J Endod. 2001 Nov;27(11):683-6. — View Citation

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. — View Citation

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. — View Citation

Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Özyürek 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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	accuracy of detection of MB2	detection of MB2 on CBCT images of maxillary first molars using deep learning model	baseline