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临床试验/NCT07587060
NCT07587060尚未招募不适用

Development and Diagnostic Accuracy of a Deep Learning Model for Root Canal Curvature Analysis in Mandibular Molars Using CBCT Scans: A Diagnostic Accuracy Study

Cairo University1 个研究点 分布在 1 个国家目标入组 207 人开始时间: 2026年6月1日最近更新:

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Samaa Mohammed Osman Mohammed

lecturer assistant at Department of Endodontics, faculty of Dentistry

Cairo University

研究点 (1)

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