Artificial Intelligence Versus Conventional Digital Design for Fixed Dental Prosthesis

Официальное название

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

Обзор

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.

Подробное описание

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.

Secondary Outcome:

Margin detection accuracy will be assessed using F1 score, precision, and recall metrics.

Statistical analysis will be performed using MedCalc software (Version 22). Continuous variables will be presented as mean, root mean square, and standard deviation. Comparisons between groups will be conducted using paired t-test with a significance level set at P ≤ 0.05 (two-tailed).

The null hypothesis states that there will be no statistically significant difference between AI-designed and human-designed fixed dental prostheses.

Вмешательства

• Другое Conventional CAD-Based Fixed Dental Prosthesis Design
  Fixed dental prostheses will be digitally designed using conventional computer-aided design (CAD) software by experienced dental professionals. Designs will be based on occlusal anatomy and patient-specific intraoral scan data. These manually generated digital designs will serve as the comparator for evaluating accuracy against AI-generated designs using 3D superimposition and quantitative accuracy analysis.
• Другое Artificial Intelligence-Based Fixed Dental Prosthesis Design
  An artificial intelligence-based automated design system will generate fixed dental prosthesis designs using deep learning algorithms. The AI model will be trained (60%), validated (10%), and tested (30%) on occlusal scan datasets and historical human-designed prostheses. Generated designs will be evaluated for accuracy and marginal precision using 3D superimposition and Intersection over Union (IoU) analysis.

Первичные конечные точки

• Accuracy of AI-Designed Fixed Dental Prosthesis Compared to Human-Designed Prosthesis [Срок оценки: Immediately after crown design generation (at time of digital analysis)]

Вторичные конечные точки (1)

• Margin Detection Performance of AI System [Срок оценки: Immediately after digital crown design generation]

Критерии участия

Критерии включения

• 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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Египет · 1 центр

• MSA University — Giza

Публикации

• Wu Z, Zhang C, Ye X, Dai Y, Zhao J, Zhao W, Zheng Y. Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study. Int Dent J. 2025 Feb;75(1):127-134. doi: 10.1016/j.identj.2024.06.023. Epub 2024 Jul 28. PMID 39069456
• Ding H, Cui Z, Maghami E, Chen Y, Matinlinna JP, Pow EHN, Fok ASL, Burrow MF, Wang W, Tsoi JKH. Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dent Mater. 2023 Mar;39(3):320-332. doi: 10.1016/j.dental.2023.02.001. Epub 2023 Feb 21. PMID 36822895
• Chau RCW, Hsung RT, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent. 2024 Jun;131(6):1111-1117. doi: 10.1016/j.prosdent.2022.12.004. Epub 2023 Jan 9. PMID 36631366

Идентификаторы

NCT: NCT07432165 · REC-D 1216-5

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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