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

Misr International University

Status: Completed

Conditions: Number of Root Canals

Treatments: Diagnostic Test: Artificial Intelligence

Study type: Interventional

Funder types: Other

Identifiers

NCT06712160

MIU-IRB-2425-008

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 212 patients

Sex: All

Ages: 18 to 40 years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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212 participants in 1 patient group

CBCT, Clinical using DOM

Experimental group

Description:

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

Treatment:

Diagnostic Test: Artificial Intelligence