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骨传导植入物手术规划工具的开发与验证

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

作者信息

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

机构信息

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

Department of Otolaryngology-Head and Neck Surgery, Western University, London, ON, Canada.

出版信息

Heliyon. 2024 Mar 5;10(5):e27436. doi: 10.1016/j.heliyon.2024.e27436. eCollection 2024 Mar 15.

Abstract

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).

摘要

背景

骨桥(BONEBRIDGE®,美敦力公司)是一种骨传导装置,由外部音频处理器和通过手术固定在颞骨上的内部骨传导 - 浮动质量换能器(BC - FMT)组成。由于植入物的尺寸,在某些解剖结构中其放置可能具有挑战性,因此需要进行全面的手术规划。手动规划方法费力、耗时且容易出错。本研究旨在开发并验证一种用于确定颅骨厚度的自动化算法,以辅助骨桥及其他需要类似骨厚度估计的装置的手术规划。

材料与方法

对12个尸体颞骨进行临床计算机断层扫描(CT)。开发了一种定制的Python算法,用于自动从软组织中分割出骨骼、生成3D模型并进行光线追踪以估计骨厚度。为每个样本生成了两种厚度色图:皮质到第一个气房的厚度以及直至硬脑膜的总厚度。该算法通过与专家手动测量结果进行验证,以达成共识性解释。

结果

该算法估计的骨到气的厚度(平均值 = 4.7毫米,95%置信区间[CI]为4.3 - 5.0毫米)与专家测量结果(平均值 = 4.7毫米,CI为4.4 - 5.0毫米)紧密匹配,平均绝对差(MAD)为0.3毫米。同样,该算法对硬脑膜的估计值(6.0毫米,CI为5.4 - 6.5毫米)与专家标记值(5.9毫米,CI为5.4 - 6.5毫米)相当,MAD为0.3毫米。

结论

开发了首个用于计算颞骨中到气房和硬脑膜的颅骨厚度的自动化算法。优化了色图以辅助骨桥植入的手术规划,不过该工具可推广用于辅助任何骨厚度应用的手术规划。该工具作为开源3D Slicer软件程序(www.slicer.org)的免费可用扩展发布。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9da2/10943392/7873f0616c64/gr1.jpg

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