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3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

Published on: August 4, 2020

Development and validation of a surgical planning tool for bone-conduction implants.

Evan S Simpson¹, Carlos D Salgado¹, Seyed Alireza Rohani²

¹Department of Electrical and Computer Engineering, Western University, London, ON, Canada.

|March 18, 2024

Summary

An automated algorithm accurately estimates temporal bone thickness for surgical planning of bone-conduction devices like BONEBRIDGE. This tool enhances precision and efficiency, overcoming limitations of manual methods for improved patient outcomes.

Keywords:

BONEBRIDGE Bone conduction devices Computed tomography Image analysis Surgical planning Temporal bone

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Robotic Cochlear Implantation for Direct Cochlear Access

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Published on: June 16, 2022

Area of Science:

Biomedical Engineering
Medical Imaging
Surgical Planning

Background:

The BONEBRIDGE® is a bone-conduction device requiring precise surgical placement.
Manual surgical planning for device implantation is time-consuming and error-prone.
Accurate estimation of temporal bone thickness is crucial for successful implantation.

Purpose of the Study:

To develop and validate an automated algorithm for determining temporal bone thickness.
To improve the accuracy and efficiency of surgical planning for bone-conduction devices.
To provide a tool that aids in the placement of the BONEBRIDGE and similar implants.

Main Methods:

A custom Python algorithm was developed to process clinical CT scans of temporal bones.
The algorithm automatically segmented bone, generated 3D models, and used ray-tracing to estimate thickness.
Thickness measurements were validated against expert manual measurements.

Main Results:

The algorithm achieved high accuracy in estimating bone thickness to air cells (MAD of 0.3 mm) and dura (MAD of 0.3 mm).
Automated measurements closely matched expert manual measurements.
Generated colormaps visually represent bone thickness for surgical planning.

Conclusions:

An automated algorithm for temporal bone thickness calculation was successfully developed and validated.
This tool offers a more efficient and precise alternative to manual surgical planning methods.
The algorithm is available as a free extension for 3D Slicer, applicable to various bone thickness estimation needs.