Benchmarking deep learning-designed inlay restorations across operator experience: An in vitro comparison of time efficiency, contact intensity, and contour quality
Abstract
OBJECTIVES
This in vitro study aimed to evaluate the performance of a deep learning (DL)-based workflow for designing inlays, in terms of time efficiency, contact intensity, and contour quality, by comparing it with human-based workflows. The impact of operator experience was also assessed to examine whether the DL-based workflow could reduce experience-related variability.
METHODS
A total of 25 digital scans of maxillary and mandibular arches, including posterior abutments prepared for mesial-occlusal (MO) or distal-occlusal (DO) cavities, were used to design inlays using five different workflows. Two conventional human-based workflows were used for the controls: one by a master-level user (CM) and one by a beginner-level user (CB). A commercial DL-based workflow was tested in three distinct forms: as-generated (TD), optimized by the master-level user (TDM), and optimized by the beginner-level user (TDB). The workflows were compared using evaluation metrics including time efficiency, occlusal and proximal contact intensity, and overall contour quality. The Kruskal-Wallis test and the chi-square test were used to detect statistical differences among the groups. Post hoc analyses were performed using Dunn's test and analysis of standardized residuals (α = 0.05).
RESULTS
The design time varied significantly among the groups (P < 0.001), with TD showing a significantly shorter time than CM (P < 0.001), except between CM and TDB. Significant differences in occlusal and proximal…
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