Mapping Error Propagation in Intraoral Scanning Using Reason's Swiss-Cheese Model: An In Vitro Study of Precision...

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 scanner-specific dispersion patterns, including differences in RMS magnitude and tail behavior (percentile spread), suggesting scanner-specific patterns of residual distortion under the tested conditions. Tolerance-based metrics further showed that threshold selection materially influences interpretability and perceived clinical relevance. In vitro IOS precision assessed under repeatability conditions should be interpreted as an emergent output of multiple interacting defensive layers rather than as the isolated performance of a single component. Coupling deviation-based precision metrics with Reason's layered defense model yields a clinically actionable framework for quality control, helping anticipate where residual risk is most likely to accumulate and where mitigation checkpoints can be implemented. Conclusions: In vitro IOS repeatability should be interpreted as an emergent output of multiple interacting defensive layers rather than the isolated performance of a single component. Coupling repeatability metrics with Reason's layered defense model supports a framework for quality-oriented interpretation, helping anticipate where residual risk is most likely to accumulate and where mitigation checkpoints can be implemented.