AI Detection Model of Extra Root Canals in Mandibular Premolars Using CBCT Scans
Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.
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.
Aperçu de l'étude
Statut
Les conditions
Intervention / Traitement
Description détaillée
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
Type d'étude
Inscription (Estimé)
Phase
- N'est pas applicable
Contacts et emplacements
Coordonnées de l'étude
- Nom: Ayah Tarek, PHD candidate
- Numéro de téléphone: 20201221902479
- E-mail: ayahtarek94@gmail.com
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
La description
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
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Diagnostique
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Aucun (étiquette ouverte)
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
|---|---|
|
Expérimental: Mandibular premolars with single canals
|
It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars
|
|
Expérimental: Mandibular premolars with more than one canal
|
It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars
|
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars
Délai: During the procedure
|
Diagnostic accuracy of the AI model will be determined by comparison with expert radiologist assessment.
|
During the procedure
|
Mesures de résultats secondaires
Mesure des résultats |
Délai |
|---|---|
|
Sensitivity of the AI Model Specificity of the AI Model Positive Predictive Value (PPV) Negative Predictive Value (NPV)
Délai: During the procedure
|
During the procedure
|
Collaborateurs et enquêteurs
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Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Estimé)
Achèvement primaire (Estimé)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Autres numéros d'identification d'étude
- 7.1.1
Plan pour les données individuelles des participants (IPD)
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