Esthetics, Dental
Conditions
Keywords
Artificial intelligence, Accuracy, Smile Design, Patient satisfaction
Brief summary
The study aims to assess the accuracy and patient satisfaction of smile designs based on artificial intelligence versus conventional DSD.
Detailed description
Advances in digital technology are transforming dental esthetic treatments, particularly through the use of Digital Smile Design (DSD) software. These programs enable dentists to create personalized, natural-looking smile designs, improving both treatment planning and patient satisfaction. Initially, tools like PowerPoint and Photoshop were used for smile design, but modern DSD programs allow dentists to work with high-resolution 3D models to design smiles that align with facial features. Digital smile design (DSD) technology allows dentists to create and preview new smile designs before treatment, aiding in detailed planning and clear communication with patients. This process consists of three main steps: (1) capturing digital images or videos to assess the patient's current smile, (2) analyzing these images to identify aesthetic needs, and (3) using digital tools to simulate the planned changes. Although these digital tools have greatly improved patient experience, they can be challenging to adopt in routine practice due to the required time, skill, and cost. However, this procedure could be time consuming and subjective to the dentist's skills and expertise. To address this, artificial intelligence (AI) has been integrated into smile design software, automating tasks like facial analysis, image alignment, and smile design simulation. Accessible through apps or cloud-based platforms, AI software supports a variety of tasks, including identifying anatomical landmarks, adjusting images, and conducting live treatment simulations. DSD's integration with artificial intelligence (AI) offers further advancements, promising rapid, automated esthetic evaluations and smile designs Although AI-powered smile design tools are becoming popular in dental practices, some concerns exist that these tools may be used more for marketing purposes than for identifying genuine patient needs.
Interventions
DEVICESmilefy
AI based DSD
DEVICEEXOCAD
Conventional DSD software
Sponsors
Cairo University
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 30 Years
Healthy volunteers
No
Inclusion criteria
* Patients with esthetic problems that require digital smile design. * Patient's age ranging from 19-30 * Good oral hygiene. * Patients who have stable occlusion.
Exclusion criteria
* Poor oral hygiene. * Patients with high caries or high plaque index. * Patients with periodontal problems. * Heavy bruxism habit or presence of any parafunctional habits. * Pregnant or lactating women. * Participating in another trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy | immediately after the procedure | Mean difference between the two designs measured in millimeters (mm) using geomagic control software. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Patient satisfaction | immediately after the procedure | Visual analogue scale (VAS) from 0 to 10 (0 is the least satisfaction, 10 is the maximum satisfaction) |
Countries
Egypt
Outcome results
None listed