The Accuracy of Artificial Intelligence in the Detection of Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 117
- 试验地点
- 1
- 主要终点
- Segmentation accuracy of deep learning model compared to manual ground truth
研究概览
简要总结
The goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. This condition is a key anatomical variation that, if missed, may cause surgical complications such as nerve injury.
The study uses previously collected CBCT scans of individuals aged 15 and older from the Oral and Maxillofacial Radiology Department at Cairo University. The scans will be analyzed retrospectively.
The main questions it aims to answer are:
How closely does the AI model's segmentation of the mandibular canal match the expert manual segmentation?
How accurate is the AI model in identifying the presence or absence of bifid mandibular canals?
Participants are not actively involved. Instead, anonymized CBCT data will be analyzed using the AI model and compared to expert annotations to measure diagnostic performance.
详细描述
This retrospective diagnostic accuracy study aims to evaluate the performance of a novel artificial intelligence (AI) model in the segmentation and detection of bifid mandibular canals (BMC) on cone-beam computed tomography (CBCT) scans. The study will be conducted on anonymized DICOM data collected from a dental radiology archive, with no direct patient contact or intervention.
Study Rationale and Background Accurate identification of bifid mandibular canals is crucial for avoiding complications during oral surgical procedures. Manual identification on CBCT by experts is the current standard, yet it is time-consuming and subject to interobserver variability. Deep learning-based models have the potential to offer automated, reproducible, and efficient alternatives.
AI Model and Methodology The AI model used in this study will be developed using a 3D U-Net architecture implemented in the MONAI framework. Manual segmentation of mandibular canals, including bifid variants, will be performed using 3D Slicer software to serve as ground truth data. The AI model will be trained and validated using this ground truth.
Technical Workflow Data Collection: Retrospective DICOM CBCT images will be extracted and anonymized.
Manual Segmentation: Gold standard segmentation will be performed manually by an expert using 3D Slicer.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 15 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •CBCT scans for Egyptian people older than 15 years old (both male and female subjects are to be included)
- •The field of view of the selected scans should clearly show at least one mandibular canal completely (from the mandibular foramen to the mental foramen)
排除标准
- •CBCT images of sub-optimal quality or artifacts/high scatter interfering with the proper assessment of the mandibular canal course.
- •CBCT scans showing significant mandibular deformities.
- •CBCT scans showing severe bone loss that extends to the mandibular canal.
结局指标
主要结局
Segmentation accuracy of deep learning model compared to manual ground truth
时间窗: immediately after the intervention
Evaluation of the similarity between the AI-generated segmentation of the mandibular canal and the expert manual segmentation using the Dice similarity coefficient.
次要结局
- Diagnostic accuracy of the AI model in detecting bifid mandibular canals(immediately after the intervention)
研究者
Sara Reda Abdelhamid Aboseif
Resident, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University
Cairo University