Clinical Research Directory

AI-Assisted vs. Conventional Digital Smile Design for Laminate Veneers: A Prospective Crossover Study

Sponsor: King Salman International university

Summary

Background: Digital Smile Design (DSD) has become an important component of contemporary esthetic dentistry, allowing clinicians to visualize proposed treatment outcomes and improve communication with patients and dental technicians. Conventional computer-aided design (CAD) workflows, such as Exocad, provide individualized designs but require considerable operator expertise and time and may be influenced by operator-related variability. Artificial intelligence (AI)-assisted CAD systems have recently been introduced to automate parts of the design process and potentially improve efficiency, consistency, and esthetic predictability. However, direct clinical comparisons between AI-assisted and conventional CAD workflows for laminate veneer smile design remain limited. Aim: This prospective randomized crossover study aims to compare AI-assisted and conventional manual CAD workflows for digital smile design in patients indicated for maxillary anterior laminate veneers, focusing on patient-reported satisfaction, esthetic and functional design outcomes, and software efficiency. Methods: A total of 44 adult participants indicated for laminate veneer treatment were recruited from the Oral and Dental Diagnostic Center, Faculty of Dentistry, King Salman International University. The sample size includes allowance for an anticipated 20% attrition. Participants will be randomly allocated in a 1:1 ratio to one of two crossover sequences: AI-assisted CAD followed by conventional CAD, or conventional CAD followed by AI-assisted CAD. A washout period of at least 48 hours will separate the two mock-up evaluations. Standardized DSD photographs and intraoral scans will be obtained for each participant. Two digital smile designs will then be produced: one using AI-assisted software and the other using the conventional manual Exocad Smile Creator workflow. Both designs for each case will be completed under standardized conditions to minimize operator-related variability. The resulting virtual designs will be 3D printed to fabricate silicone indices for intraoral mock-up trials. Participants will evaluate both mock-ups while remaining blinded to the design method. Outcomes: The primary outcome will be patient esthetic satisfaction with each mock-up, assessed using a Visual Analog Scale (VAS; 0-10). Secondary outcomes will include objective design-based esthetic parameters, including facial landmark and plane accuracy, tooth proportions, symmetry, gingival zenith symmetry, incisal edge position, midline alignment, and contour accuracy. Functional outcomes will include occlusal contacts, incisal guidance, and interproximal contact length. Software efficiency will also be assessed through design time and operator-rated ease of use. Three blinded prosthodontists will evaluate the esthetic and functional characteristics of the virtual designs. Statistical Analysis and Ethics: Data normality will be evaluated using the Shapiro-Wilk test, followed by appropriate parametric or non-parametric statistical analyses. The protocol reviewed by the Research Ethics Committee of the Faculty of Dentistry, King Salman International University. Written informed consent obtained from all participants, and collected data will be coded, securely stored, and treated confidentially. Expected Significance: The study is designed to provide evidence on whether AI-assisted CAD workflows differ from conventional CAD workflows in patient satisfaction, esthetic and functional design outcomes, and workflow efficiency. By integrating patient-centered intraoral mock-up evaluation with objective virtual-model assessment in a crossover design, the study may help inform the evidence-based incorporation of AI into digital workflows for laminate veneer treatment.

Official title: AI-assisted Versus Conventional CAD Workflows in Digital Smile Design for Laminate Veneers: A Prospective Crossover Study of Esthetic and Patient-related Outcomes

Key Details

Conditions

Interventions

Locations (1)