The Accuracy of Detection of Artificial Intelligence Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
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 .
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| CBCT Images of Maxillary 1st molars
|
Diagnostic Test: deep learning model
deep learning model developed by computer science expert and based on convolution neural network , and trained by our datasets.
Other Names:
|
Outcome Measures
Primary Outcome Measures
- accuracy of detection of MB2 [baseline]
detection of MB2 on CBCT images of maxillary first molars using deep learning model
Eligibility Criteria
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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Faculty of dentistry cairo university | Cairo | Egypt | 12611 |
Sponsors and Collaborators
- Cairo University
Investigators
- Study Director: Enas Anter, Ph.D, Cairo University
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- CBCT AI 7-1-1