Development and Diagnostic Accuracy of a Deep Learning Model for Root Canal Curvature Analysis in Mandibular Molars Using CBCT Scans: A Diagnostic Accuracy Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 207
- 试验地点
- 1
研究概览
简要总结
Root canal preparation in endodontics poses significant challenges, particularly in curved canals of mandibular molars, where accurate preoperative assessment using CBCT imaging is crucial to avoid iatrogenic errors and improve treatment outcomes. This study aims to develop and evaluate the diagnostic accuracy of a deep learning model for analyzing root canal curvature angles in mandibular molars from CBCT scans, compared to human expert evaluations. The model will leverage advanced AI techniques to segment and measure curvatures objectively, addressing limitations in manual interpretation, potentially standardizing case difficulty assessments and aiding clinical decision-making.
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
- 18 Years 至 65 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old
- •Small Field of view (FOV) including maximum a quadrant
- •Voxel size not larger than 2mm
- •Mandibular molars showing complete root formation
- •Carious or non-carious teeth
- •Absence of artifacts, dental implants in the adjacent teeth
排除标准
- •Mandibular first and second molars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries
- •CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment
研究者
Samaa Mohammed Osman Mohammed
lecturer assistant at Department of Endodontics, faculty of Dentistry
Cairo University
