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临床试验/NCT07432165
NCT07432165招募中不适用

Accuracy Assessment of Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis: (An Invitro Study)

October University for Modern Sciences and Arts1 个研究点 分布在 1 个国家目标入组 1,000 人开始时间: 2025年6月15日最近更新:
干预措施

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
1,000
试验地点
1
主要终点
Accuracy of AI-Designed Fixed Dental Prosthesis Compared to Human-Designed Prosthesis

研究概览

简要总结

This in vitro study aims to evaluate the accuracy of an Artificial Intelligence (AI)-based automatic design system for fixed dental prosthesis (FDP) compared with conventional computer-aided design (CAD) software. Digital scans of teeth requiring fixed dental prosthesis will be collected and used to generate prosthetic designs using two approaches: human-designed CAD restorations and AI-generated restorations.

The primary outcome is design accuracy assessed using 3D superimposition and Intersection over Union (IOU) percentage. Secondary outcomes include margin detection performance measured using F1 score, precision, and recall. A total sample size of 438 scans will be analyzed.

The study will determine whether AI-generated prosthesis designs demonstrate comparable accuracy to conventional digital designs.

详细描述

This study is designed as an in vitro comparative study to assess the accuracy and performance of an Artificial Intelligence (AI)-based automatic design system for fixed dental prosthesis (FDP) in comparison with conventional computer-aided design (CAD) software.

Digital scans of patients requiring fixed dental prosthesis will be collected from the production laboratory of the Faculty of Dentistry. Eligible scans will include adults aged 18-65 years with damaged teeth requiring FDP and adequate occlusal anatomy for analysis.

The AI workflow consists of three sequential phases: training (60%), validation (10%), and testing (30%). The AI model will be trained using natural spatial tooth morphology and historical human-designed FDP datasets. The conventional group will consist of FDPs manually designed by experienced dental professionals using CAD software.

Primary Outcome:

The primary outcome is crown design accuracy measured using 3D superimposition analysis and quantified using Intersection over Union (IOU) percentage.

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Diagnostic
盲法
None

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

  • Adults aged 18-65 years Patients with a damaged tooth requiring a fixed dental prosthesis Available digital intraoral scans Adequate occlusal anatomy for analysis of opposing teeth

排除标准

  • Incomplete or poor-quality digital scans Severe occlusal abnormalities affecting analysis Patients outside the specified age range

研究组 & 干预措施

Human-Designed Fixed Dental Prosthesis (Conventional CAD)

Other

Fixed dental prostheses will be designed manually using conventional CAD software by experienced dental professionals based on occlusal anatomy and patient-specific scan data. The designs will serve as the control comparator to evaluate accuracy against AI-generated designs using 3D superimposition analysis.

干预措施: Conventional CAD-Based Fixed Dental Prosthesis Design (Other)

AI-Designed Fixed Dental Prosthesis

Other

Fixed dental prostheses will be automatically generated using an artificial intelligence-based design system. The AI model will be trained, validated, and tested using occlusal scan datasets and historical human-designed prostheses. The generated designs will be evaluated for accuracy using 3D superimposition and Intersection over Union (IoU) analysis.

干预措施: Artificial Intelligence-Based Fixed Dental Prosthesis Design (Other)

结局指标

主要结局

Accuracy of AI-Designed Fixed Dental Prosthesis Compared to Human-Designed Prosthesis

时间窗: Immediately after crown design generation (at time of digital analysis)

Accuracy will be assessed by superimposing AI-generated crown designs and human-designed crowns using 3D imaging software. The Intersection over Union (IOU) percentage will be calculated to evaluate morphological agreement and occlusal fit between the two design approaches.

次要结局

  • Margin Detection Performance of AI System(Immediately after digital crown design generation)

研究者

发起方
October University for Modern Sciences and Arts
申办方类型
Other
责任方
Principal Investigator
主要研究者

Tarek Adham Saad Ahmed El-Shammaa

Master student of Prosthodontics

October University for Modern Sciences and Arts

研究点 (1)

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