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Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars

NCT ID: NCT06712160

Last Updated: 2024-12-02

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

212 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-01-20

Study Completion Date

2024-02-20

Brief Summary

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The study compares the effectiveness of Artificial Intelligence (AI), CBCT, and clinical examination in detecting root canals in upper first, upper second, and lower first molars. Results show AI detects more molars with three or four canals in conventional treatment cases and retreatment cases.

Detailed Description

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Introduction: Accurate root canal detection is crucial for successful endodontic treatment, particularly in complex molar cases. Conventional methods, such as clinical examination and cone-beam computed tomography (CBCT), have their limitations, as high radiation exposure. Recent advancements in Artificial Intelligence (AI) have shown promise in improving diagnostic accuracy. This study aims to compare the effectiveness of AI, CBCT, and clinical examination using a dental operating microscope (DOM) in detecting root canals in upper first, upper second, and lower first molars, in both conventional and retreatment cases. Methods: CBCT scans from 210 patients requiring non-surgical root canal therapy or re-treatment were selected. The scans were analyzed using three detection methods: clinical examination via DOM, interpretation by two experienced endodontists using CBCT, and an AI convolutional neural network (CNN) software (Diagnocat). The detected number of root canals was recorded and compared across the three methods.

Conditions

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Number of Root Canals

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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CBCT, Clinical using DOM

Comparing the three methods for the detection of the number of canals of maxillary and mandibular molars

Group Type EXPERIMENTAL

Artificial Intelligence

Intervention Type DIAGNOSTIC_TEST

The number of canals detected by AI

Interventions

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Artificial Intelligence

The number of canals detected by AI

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Male and female patients who were capable of providing informed consent
* Age between 18 to 40 years old.
* A restorable tooth.

Exclusion Criteria

* Patients that underwent vital pulp therapies.
* Patients with calcifications in pulp space.
* Open apex/immature roots.
* Teeth restored by full coverage crowns.
* Pregnant women by taking adequate history from patient and pregnancy test that was done in the first visit
Minimum Eligible Age

18 Years

Maximum Eligible Age

40 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Misr International University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Misr International University

Cairo, , Egypt

Site Status

Countries

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Egypt

Provided Documents

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Document Type: Study Protocol

View Document

Document Type: Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

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MIU-IRB-2425-008

Identifier Type: -

Identifier Source: org_study_id