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NCT Number: NCT07689708

AI Detection Model of Extra Root Canals in Mandibular Premolars Using CBCT Scans

Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which exhibit considerable anatomical variability and may contain additional root canals that are difficult to detect using conventional diagnostic methods.

Cone Beam Computed Tomography (CBCT) provides three-dimensional visualization of root canal anatomy and has significantly improved the detection of anatomical variations. However, interpretation of CBCT images remains dependent on the experience and expertise of the clinician, leading to potential observer variability and missed diagnoses.

Recent advances in artificial intelligence (AI), particularly deep learning models based on convolutional neural networks, have shown promising results in dental image analysis and diagnostic support. AI-assisted diagnostic systems may improve the accuracy, consistency, and efficiency of CBCT interpretation by automatically identifying complex anatomical structures.

The aim of this retrospective diagnostic accuracy study is to evaluate the performance of a newly developed deep learning model for the detection of extra root canals in mandibular premolars using CBCT images. The diagnostic accuracy of the AI model will be assessed by comparing its findings with the assessments of experienced oral and maxillofacial radiologists, which will serve as the reference standard.

A total of 272 CBCT scans of mandibular premolars from Egyptian patients will be included according to predefined eligibility criteria. Diagnostic performance will be evaluated using measures including sensitivity, specificity, positive predictive value, and negative predictive value.

The findings of this study may provide evidence regarding the clinical applicability of AI-assisted diagnostic tools in endodontics and contribute to improved detection of complex root canal anatomy, reduced incidence of missed canals, and enhanced treatment outcomes.

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Key information

Conditions

Extra Canals

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

The goal of this observational study is to evaluate whether a deep learning artificial intelligence (AI) model can accurately detect extra root canals in mandibular premolars using Cone Beam Computed Tomography (CBCT) images in Egyptian patients. The main questions it aims to answer are:

Can the AI model accurately detect extra root canals in mandibular premolars on CBCT scans?
Is the diagnostic accuracy of the AI model comparable to that of experienced oral and maxillofacial radiologists? Researchers will compare the results generated by the AI model with the assessments of experienced radiologists, which will serve as the reference standard.

Participants will:

Provide previously acquired CBCT scans that meet the study eligibility criteria.
Have their CBCT images analyzed by the AI model.
Have their CBCT images independently evaluated by experienced radiologists for comparison with the AI findings.

The study findings may help determine the potential role of AI-assisted diagnostic tools in improving the detection of complex root canal anatomy and supporting endodontic diagnosis

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old
Small Field of view (FOV) including maximum a quadrant
Voxel size not larger than 2mm
Mandibular premolars showing complete root formation
Carious or non-carious teeth
Absence of artifacts.

Exclusion criteria

Mandibular first and second premolars 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

Treatment and study plan

AI Model to detect any extra canals in mandibular premolars

Diagnostic Test

It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars

Primary outcomes

Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars

Time frame: During the procedure

Diagnostic accuracy of the AI model will be determined by comparison with expert radiologist assessment.

Secondary outcomes

Sensitivity of the AI Model Specificity of the AI Model Positive Predictive Value (PPV) Negative Predictive Value (NPV)

Time frame: During the procedure

Study contacts

Contact information is provided by the study sponsor or research team.

Ayah Tarek, PHD candidate

CONTACT

[email protected]

20201221902479

Sponsors and collaborators

Lead sponsor

Cairo University

Other

Registry information

Official study title

Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2027
First posted: Jul 8, 2026
Registry last updated: Jul 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.