The Accuracy of Computer Aided Detection of Periapical Radiolucencies on Cone -Beam Computed Tomography Images Using Artificial Intelligence

NCT05538104 | Unknown

• 1 other identifier: ORAD 7,1,1
• Study Type: observational
• Target: 50
• Locations: 0 countries
• Sites: N/A

Brief Summary

A diagnostic accuracy study to assess the accuracy of a newly developed deep learning model in the automatic detection of periapical radiolucent lesions of upper and lower jaws by comparing it with experienced radiologists' opinion, which represents the ground truth. Hypothesis: The null hypothesis is that the results of the deep learning model are as accurate as the radiologists' opinion.

Conditions

Periapical Lesions

Outcome Measures

Primary Outcomes (1)

• • Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.

  * The computer generated deep learning model. * Well experienced radiologists' vision and interpretation of CBCT images in optimum viewing conditions. Using CBCT viewer software program Blue Sky Bio. unit : yes or no

  1year

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The CBCT data of this study will be obtained from the CBCT data base available at the department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, Egypt and from available online data set with different CBCT machines. CBCT scans of Egyptian patients who have already been subjected to CBCT examination as part of their dental diagnosis and/or treatment planning will be included according to the proposed eligibility criteria. .

You may qualify if:

• CBCT scans of maxilla and mandible with good quality free pf periapical radiolucent lesions .
• CBCT scans of maxilla and mandible with good quality showing periapical radiolucent lesions

You may not qualify if:

• CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.

Sponsors & Collaborators

• Cairo University: lead

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: phd candidate

Study Record Dates

• First Submitted: September 9, 2022
• First Posted: September 13, 2022
• Study Start: September 1, 2022
• Primary Completion: August 1, 2023
• Study Completion: December 1, 2023
• Last Updated: September 13, 2022

Record last verified: 2022-09