The Accuracy of Computer Aided Detection of Periapical Radiolucencies on Cone -Beam Computed Tomography Images Using Artificial Intelligence: Diagnostic Accuracy Study.
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Sponsor
- Cairo University
- Enrollment
- 50
- Primary Endpoint
- • Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.
Study Overview
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.
Detailed Description
- Study Design: A diagnostic accuracy study
- Setting and Location: Retrospective data collection is planned before the index test and reference standard are to be performed. 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.
B) Participants:
Based on sample size calculation, a sample of 50 periapical radiolucent lesions of upper and lower different locations in jaw found in CBCT scans. The selection of the scans to be included will be based on the following eligibility criteria.
Inclusion criteria:
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Retrospective
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •• 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
Exclusion Criteria
- •• CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.
Outcomes
Primary Outcomes
• Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.
Time Frame: 1year
* 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
Secondary Outcomes
No secondary outcomes reported
Investigators
Yasmin Aboulmaaty
phd candidate
Cairo University