Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 123
- 主要终点
- diagnostic accuracy
研究概览
简要总结
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Periapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.
排除标准
- •Deciduous teeth, non-restorable, non-treated teeth
研究组 & 干预措施
Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
This study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system.
干预措施: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs (Diagnostic Test)
结局指标
主要结局
diagnostic accuracy
时间窗: From Data collection to model testing up to 60 weeks
Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level
次要结局
未报告次要终点
研究者
Noha Mohamed Elsaber
Principal Investigator
Cairo University