Unveiling the Intricacies of the Inner Ear Anatomy: Novel 3D-Printed Model for Detailed Visualization and Functional Demonstrations.

  • Published In: Journal of Laryngology & Otology, 2024, v. 138, n. 7. P. 787 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wu, Shou-Wu; Nian, Zhong-Zhu; Lin, Wen; Zhang, Xiao-Dong 3 of 3

Abstract

Objectives: This research aimed to print realistically detailed and magnified three-dimensional models of the inner ear, specifically focusing on visualising its complex labyrinth structure and functioning simulation. Methods: Temporal bone computed-tomography data were imported into Mimics software to construct an initial three-dimensional inner-ear model. Subsequently, the model was amplified and printed with precision using a three-dimensional printer. Five senior attending physicians evaluated the printed model using a Likert scale to gauge its morphological accuracy, clinical applicability and anatomical teaching value. Results: The printed inner-ear model effectively demonstrated the intricate internal structure. All five physicians agreed that the model closely resembled the real inner ear in shape and structure, and simulated certain inner-ear functions. The model was considered highly valuable for understanding anatomical structure and disorders. Conclusion: The three-dimensionally printed inner-ear model is highly simulated and provides a valuable visual tool for studying inner-ear anatomy and clinical teaching, benefiting otologists. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Laryngology & Otology. 2024/07, Vol. 138, Issue 7, p787
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2024
  • ISSN:0022-2151
  • DOI:10.1017/S0022215124000367
  • Accession Number:180094839
  • Copyright Statement:Copyright of Journal of Laryngology & Otology is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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