Mapping Error Propagation in Intraoral Scanning Using Reason's Swiss-Cheese Model: An In Vitro Study of Precision Under Repeatability Conditions
Abstract
Background: Intraoral scanning (IOS) errors seldom originate from a single-point failure; instead, they arise from interactions among hardware performance, reconstruction software, and operator-dependent acquisition behaviors. James Reason's layered defense (Swiss-cheese) model provides a systems-oriented lens to trace how residual vulnerabilities can propagate into clinically relevant surface distortions. Objectives: To quantify within-scanner precision of IOS under repeatability conditions in a controlled in vitro setting and to propose a Reason-based framework that maps defensive layers, barriers, and residual failure modes along the IOS workflow. Methods: A controlled in vitro design was implemented to minimize clinical confounders. A standardized partially edentulous maxillary reference specimen was scanned repeatedly with three IOS systems under fixed environmental conditions using a standardized scanning strategy. Within each IOS, precision was quantified from repeated scans using surface deviation metrics, including root mean square (RMS) deviation, percentile-based dispersion, and the percentage of points within a predefined tolerance band. Residual vulnerabilities were organized into a systems-oriented error framework by defensive layer (hardware, software/processing, and acquisition/operator) and workflow stage (pre-scan preparation, acquisition, reconstruction/registration, and export/verification). Results: Deviation-based precision metrics revealed…
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