Accuracy of Artificial Intelligence Technology in Detecting Periapical Lesions in Human Teeth. (AI)

Future University in Egypt

Status: Completed

Conditions: Periapical Lesion

Treatments: Other: Cone beam computed tomography; Other: artificial intelligence software

Study type: Interventional

Funder types: Other

Identifiers

NCT07319182

(52)/11-2024

Details and patient eligibility

About

Aims to evaluate the accuracy of using Artificial intelligence software in detecting the presence of periapical lesion compared to CBCT imaging.

Full description

In this research, patients referred to endodontic department in the university will undergo clinical examination (percussion, palpation) tests. These will be recorded. Then the patient will undergo a periapical radiograph to detect the presence of periapical lesion. Patients with periapical lesions will then undergo a cone beam computed tomography and this scan will be uploaded into the Artificial intelligence software to detect the accuracy of the software in detecting the presence of the periapical lesion. Any patient with a periapical lesion in need of root canal treatment will undergo the treatment.

Enrollment: 100 patients

Sex: All

Ages: 18 to 50 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

1. All patients must be medically free from any systemic disease that can affect root canal treatment.
2. 18 to 50 years old patients with permanent teeth presenting with periapical pathosis.
3. No sex predilection.
4. All patients must have good oral hygiene.
5. Restorable teeth
6. Positive patient's acceptance for participating in the study.
7. Patients able to sign informed consent

Exclusion criteria

1. Patients above 50 years or patients below 18 years.
2. Patients with very poor oral hygiene.
3. Pregnant women after taking detailed history and pregnancy test must be in the first visit.
4. Psychologically disturbed patients.

Teeth that have:

• Periodontally affected with grade 2 or 3 mobility.
• Not restorable teeth.
• Abnormal anatomy and calcified canals

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Sequential Assignment

Masking: Single Blind

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100 participants in 2 patient groups

cone beam computed tomography

Active Comparator group

Description:

detecting radiolucent periapical lesions using cone beam computed tomography

Treatment:

Other: Cone beam computed tomography

artificial intelligence software

Active Comparator group

Description:

Cone beam CT scan will be uploaded to artificial intelligence software to ensure that the software will detect radiolucent periapical lesion compared to Cone beam CT scans.

Treatment:

Other: artificial intelligence software