Recent advancements in artificial intelligence (AI) have revolutionized various fields, including dentistry, where AI systems are being promoted to reduce design time for dental restorations. However, it remains unclear whether this increased speed compromises critical factors such as marginal fit accuracy, occlusion, and contact points-elements crucial for the success of dental prosthetics. The existing scientific evidence regarding the efficiency of AI-supported dental design systems is limited. A review of the literature revealed only one clinical study addressing this aspect, highlighting the urgent need for further comparative research.
This clinical research aims to Evaluate Crowns Designed by two Artificial Intelligence computer-aided AIـCAD Versus Crowns Designed by Traditional Design Software One Traditional CAD by assessing the following metrics:
- Marginal fit accuracy
- Quality of contact points
- Occlusion
Importance of the Research
Given the scarcity of studies focusing on AI-based crown design systems clinically, this research will assist dental clinics and laboratories in making informed decisions regarding the adoption of AI technologies versus continuing with traditional methodologies.
Study Design
self controlled clinical trial
Clinical Procedures
- Tooth Preparation: Prepare the teeth to receive a zirconia crown.
- Impression Taking: Obtain impressions using silicone.
- Digital Conversion: Convert impressions into a digital model using a laboratory scanner.
- Designing : Send the digital model to selected crown design programs (both AI-driven and traditional).
- Crown Fabrication: After design approval, crowns will be produced from PMMA using a milling unit.
- Clinical Evaluation: Conduct a clinical assessment of the crowns. Evaluation Metrics
Marginal Fit Accuracy
Measurement clinically using a probe. Comparison with silicone replica technique or Analysis through 3D modeling software (X control GEOM).
Contact Points Quality
Measure the force required to pull a dental floss between teeth.
Occlusion Assessment
Utilize 100-micron and 12-micron articulating paper and Shimstock to evaluate occlusal contact points.
Data will be analyzed using appropriate statistical methods to compare the outcomes between AI-driven designs and traditional methods, ensuring robust conclusions regarding their efficacy.
The results of this study are anticipated to provide valuable insights into the reliability and quality of AI-based crown design systems compared to traditional methods, guiding dental practices in their decision-making processes regarding technology adoption.
This proposal serves as a foundation for conducting a thorough investigation into the effectiveness of AI in dental crown design, addressing a critical gap in current research and practice
