Finite element analysis of auditory characteristics in patients with middle ear diseases

Abstract

Conclusion: This study validates that a finite element model of the human ossicular chain and tympanic membrane can be used as an effective surgical assessment tool in clinics.

Objective: The present study was performed to investigate the application of a finite element model of ossicular chain and tympanic membrane for fabrication of individualized artificial ossicles.

Methods: Twenty patients (20 ears) who underwent surgery for middle ear disease (n = 20) and 10 healthy controls (10 ears) were enrolled in the hospital. Computed tomography (CT) and pure tone audiometry were performed before and after surgery. A finite element model was developed using CT scans, and correlation analysis was conducted between stapes displacement and surgical methods. An audiometric test was also performed for 14 patients before and after surgery.

Results: Stapes displacement in the healthy group (average = 3.31 × 10⁻⁵ mm) was significantly greater than that in the impaired group (average = 1.41 × 10⁻⁶ mm) prior to surgery. After surgery, the average displacement in the impaired group was 2.55 × 10⁻⁶ mm, which represented a significant improvement. For the patients who underwent the audiometric test, 10 improved hearing after surgery, and stapes displacement increased in nine of these 10 patients.

Chinese abstract

结论 本研究验证了人类听觉小骨链和鼓膜的有限元模型可用作诊所有效手术评估的工具。

目的 本研究的目的是研究应用有限元模型的听小骨链和鼓膜为个人特制人工小骨。

方法 20位接受中耳病手术（20个耳朵）的患者和10位作为对照的健康人（10个耳朵）注册入院。在手术前后进行计算机断层扫描（CT）和纯音听力测量。使用CT扫描开发有限元模型, 并进行了镫骨位移和手术方法之间的相关分析。还对14名患者在手术前后进行听力测试。

结果 手术前的健康组的镫骨位移（平均= 3.31 × 10-5mm）显著大于受损组（平均= 1.41 × 10-6mm）。手术后, 受损组平均位移为2.55 × 10-6 mm, 表明显著改善。对于接受听力测试的患者, 10位在术后改善了听力。在这10位患者中, 有9位的镫骨位移增加了。

Keywords:

• Middle ear disease
• computed tomography
• finite element model
• stapes
• pure tone audiometry

Acknowledgements

This research was supported by Guangdong Province Natural Sciences Fund Project Funding (No. 2014A030313373) and The Fundamental Research Funds for the Central Universities (No. 21612444). We would like to thank Dr Huang Ning, PhD (alumnus of Department of Biomedical Engineering, Rutgers University, NJ, USA, previously Research Scientist at Siemens Corporate Research in Princeton, NJ, USA, and currently Research Scientist at GE Healthcare, China) for his helpful suggestions and review of this article.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Additional information

Funding

This research was supported by Guangdong Province Natural Sciences Fund Project Funding (No. 2014A030313373) and The Fundamental Research Funds for the Central Universities (No. 21612444).