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

Evan S. Simpson; Carlos D. Salgado; Seyed Alireza Rohani; Sumit K. Agrawal; Hanif M. Ladak

Heliyon (Mar 2024)

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

Evan S. Simpson,
Carlos D. Salgado,
Seyed Alireza Rohani,
Sumit K. Agrawal,
Hanif M. Ladak

Affiliations

Evan S. Simpson: Department of Electrical and Computer Engineering, Western University, London, ON, Canada
Carlos D. Salgado: Department of Electrical and Computer Engineering, Western University, London, ON, Canada
Seyed Alireza Rohani: Department of Otolaryngology-Head and Neck Surgery, Western University, London, ON, Canada
Sumit K. Agrawal: Department of Electrical and Computer Engineering, Western University, London, ON, Canada; Department of Otolaryngology-Head and Neck Surgery, Western University, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; School of Biomedical Engineering, Western University, London, ON, Canada
Hanif M. Ladak: Department of Electrical and Computer Engineering, Western University, London, ON, Canada; Department of Otolaryngology-Head and Neck Surgery, Western University, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; School of Biomedical Engineering, Western University, London, ON, Canada; Corresponding author. Department of Medical Biophysics Western University 1151 Richmond Street London, Ontario, N6A 3K7, Canada.

Journal volume & issue: Vol. 10, no. 5
p. e27436

Abstract

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Background: The BONEBRIDGE® (Med-El GmbH) is a bone-conduction device comprising an external audio processor and an internal Bone Conduction-Floating Mass Transducer (BC-FMT) surgically anchored to the temporal bone. Due to the implant's size, its placement may be challenging in certain anatomies, necessitating thorough surgical planning. Manual planning methods are laborious, time-intensive, and prone to errors. This study aimed to develop and validate an automated algorithm for determining skull thickness, aiding in the surgical planning of the BONEBRIDGE and other devices requiring similar bone thickness estimations. Materials and methods: Twelve cadaveric temporal bones underwent clinical computed tomography (CT). A custom Python algorithm was developed to automatically segment bone from soft tissue, generate 3D models, and perform ray-tracing to estimate bone thickness. Two thickness colormaps were generated for each sample: the cortical thickness to the first air cell and the total thickness down to the dura. The algorithm was validated against expert manual measurements to achieve consensus interpretation. Results: The algorithm estimated bone-to-air thicknesses (mean = 4.7 mm, 95% Confidence Interval [CI] of 4.3–5.0 mm) that closely matched the expert measurements (mean = 4.7 mm, CI of 4.4–5.0 mm), with a mean absolute difference (MAD) of 0.3 mm. Similarly, the algorithm's estimations to the dura (6.0 mm, CI of 5.4–6.5 mm) were comparable to the expert markings (5.9 mm, CI of 5.4–6.5 mm), with a MAD of 0.3 mm. Conclusions: The first automated algorithm to calculate skull thickness to both the air cells and dura in the temporal bone was developed. Colormaps were optimized to aid with the surgical planning of BONEBRIDGE implantation, however the tool can be generalized to aid in the surgical planning of any bone thickness application. The tool was published as a freely available extension to the open-source 3D Slicer software program (www.slicer.org).

Published in Heliyon

ISSN: 2405-8440 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Science (General); Social Sciences: Social sciences (General)
Website: https://www.cell.com/heliyon/home

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