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Updated: Oct 30, 2025

Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing
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A method for finding high accuracy surface zones on 3D printed bone models.

Sebastian Andreß1, Felix Achilles1, Jonathan Bischoff1

  • 1Department of General, Trauma and Reconstructive Surgery, University Hospital, LMU Munich, Germany.

Computers in Biology and Medicine
|July 3, 2021
PubMed
Summary
This summary is machine-generated.

A new method, Similarity Subgroups Registration, accurately identifies reliable areas on 3D printed surgical models, even with printing errors. This allows safe use of partially corrupted models, improving surgical planning efficiency and reducing treatment delays.

Keywords:
3D printMedicalOrthopedicPelvisRegistrationSurgeryValidation

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Area of Science:

  • Medical Imaging and 3D Printing
  • Surgical Planning and Patient Care
  • Biomedical Engineering

Background:

  • Three-dimensional (3D) printing is increasingly used in surgery, but errors in printed models can compromise patient care.
  • Current validation methods for 3D printed models lack standardization and accuracy.
  • Ensuring the quality of 3D printed models is crucial for reliable surgical planning and diagnostics.

Purpose of the Study:

  • To present a novel method for validating 3D printed models by identifying surface deviations and high-accuracy zones.
  • To compare the proposed method's performance against existing registration techniques in orthopedic applications.
  • To enable the safe utilization of partially corrupted 3D printed models for surgical planning.

Main Methods:

  • Developed and tested a method named Similarity Subgroups Registration (SSR) to detect surface deformations in 3D printed models.
  • Artificially deformed 15 digital acetabular bone models to simulate common 3D printing errors for parameter tuning and performance evaluation.
  • Compared SSR against simple landmark registration and iterative closest point registration using 32 patient-specific 3D printed bone models.

Main Results:

  • SSR demonstrated high sensitivity (>95%) and specificity (>99%) in detecting simulated surface deformations.
  • Only 25% of re-digitized 3D printed models were free of significant deformations.
  • SSR identified 72% of models as having acceptable accuracy for specific implant locations, significantly outperforming traditional methods (9% and 41% acceptance rates).

Conclusions:

  • Similarity Subgroups Registration provides a reliable approach to assess the quality of 3D printed models for surgical applications.
  • The method allows clinicians to safely use partially corrupted models, enhancing surgical planning efficiency and reducing the need for reprints.
  • SSR has broad applicability in clinical settings and non-medical fields requiring localized high-accuracy 3D surface analysis.