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

Study Overview
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Brief Summary
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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
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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 .

Official Title
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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

Conditions
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Artificial Intelligence

Intervention / Treatment
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* Diagnostic Test: deep learning model


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| CBCT Images of Maxillary 1st molars<br> | Diagnostic Test: deep learning model<br>* deep learning model developed by computer science expert and based on convolution neural network , and trained by our datasets.<br>* Other names: artificial intelligence tool;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| accuracy of detection of MB2 | detection of MB2 on CBCT images of maxillary first molars using deep learning model | baseline |