The Accuracy of Computer Aided Detection of Periapical Radiolucencies on Cone -Beam Computed Tomography Images Using Artificial Intelligence: Diagnostic Accuracy Study.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 50
- 主要终点
- • Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.
研究概览
简要总结
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.
详细描述
- 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:
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •• 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
排除标准
- •• CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.
结局指标
主要结局
• Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.
时间窗: 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
次要结局
未报告次要终点
研究者
Yasmin Aboulmaaty
phd candidate
Cairo University