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Updated: May 28, 2026

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
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Mapping Error Propagation in Intraoral Scanning Using Reason's Swiss-Cheese Model: An In Vitro Study of Precision

Cristina-Alexandra Cozmescu1, Ana Maria Cristina Țâncu1, Lucian Toma Ciocan2

  • 1Discipline of Prosthodontics, Faculty of Dentistry, "Carol Davila" University of Medicine and Pharmacy, 37 Dionisie Lupu Street, District 2, 020021 Bucharest, Romania.

Dentistry Journal
|May 26, 2026
PubMed
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This summary is machine-generated.

Intraoral scanning (IOS) errors result from complex system interactions, not single failures. A new framework using James Reason's model helps identify and mitigate risks in IOS workflows for better quality control.

Area of Science:

  • Digital Dentistry
  • Biomedical Engineering
  • Quality Management Systems

Background:

  • Intraoral scanning (IOS) errors arise from interactions between hardware, software, and user behavior, rather than isolated failures.
  • James Reason's layered defense model offers a systems-based approach to understanding error propagation in IOS.
  • Identifying failure modes is crucial for improving the clinical reliability of digital dental impressions.

Purpose of the Study:

  • To quantitatively assess the within-scanner precision of IOS devices under controlled repeatability conditions.
  • To develop a framework based on Reason's model for analyzing IOS errors across different workflow stages.
  • To map defensive layers, barriers, and failure modes within the IOS process.

Main Methods:

Keywords:
ReasonSwiss-cheese modeldigital impressionserror mitigationintraoral scannerprecisionquality controlrepeatabilityroot mean square

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  • A controlled in vitro study using a standardized maxillary reference specimen scanned repeatedly with three IOS systems.
  • Precision was measured using surface deviation metrics (RMS deviation, percentile dispersion, tolerance bands).
  • Residual vulnerabilities were categorized by defensive layer (hardware, software, acquisition) and workflow stage.

    • Main Results:

    • IOS precision metrics showed scanner-specific dispersion patterns, indicating unique residual distortion characteristics.
    • The choice of tolerance thresholds significantly impacted the interpretation of IOS accuracy.
    • In vitro IOS precision is an emergent property of interacting system components, not isolated performance.

    Conclusions:

    • In vitro IOS repeatability reflects the interplay of multiple system layers, not just individual component performance.
    • Integrating precision metrics with Reason's layered defense model provides a framework for quality control in IOS.
    • This approach aids in anticipating residual risk accumulation and implementing effective mitigation strategies.