Evidence map›Paper›PMID 41059249›Full record

ArticleRadiology advances2025

Artificial intelligence model for automatic 3-dimensional reconstruction of ossicular chain and bony labyrinth from high-resolution CT.

Mingwei Xie, Haonan Wang, Zehong Yang, Ming Gao, Guangzi Shi, Xingnan Liao, Zhongqiang Luo, Xiaomeng Li, Jun Shen

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Article in Radiology advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Mingwei XieDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.ORCID https://orcid.org/0000-0003-1590-1381
Haonan WangDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.
Zehong YangDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Ming GaoDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Guangzi ShiDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Xingnan LiaoDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Zhongqiang LuoDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
Xiaomeng LiDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.ORCID https://orcid.org/0000-0003-1105-8083
Jun ShenDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.ORCID https://orcid.org/0000-0001-7746-5285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Three-dimensional (3D) reconstruction of ossicular chain and bony labyrinth based on temporal bone high-resolution CT (HRCT) is useful for diagnosis and treatment guidance of middle and inner ear diseases. However, these structures are small and irregular, making manual reconstruction time-consuming. Purpose: To develop and validate an artificial intelligence (AI) model based on semisupervised learning for automated 3D reconstruction of ossicular chain and bony labyrinth on HRCT images. Methods: HRCT images from 304 ears of 152 consecutive patients retrospectively collected from a single center were randomly divided into training (246 ears), validation (28 ears), and internal test (30 ears) cohorts for model development. A novel semisupervised ear bone segmentation framework was used to train the AI model, and its performance was evaluated by Dice similarity coefficients. The trained algorithm was applied to a temporally independent test dataset of 30 ears of 15 patients from the same center for comparison with manual 3D reconstruction for processing time, target volume, and visual assessment of segmentation. Results: The AI model demonstrated a Dice score of 0.948 (95% CI, 0.940-0.955) for the internal and 0.979 (95% CI, 0.973-0.986) for the temporally independent test sets. In the latter dataset, the AI model required 2% or less processing time of manual 3D reconstruction for each ear (17.7 seconds ± 10.1 vs 1080.5 seconds ± 149.8; Conclusion: The AI model enables robust, efficient and accurate 3D reconstruction for the small structures such as ossicular chain and bony labyrinth on HRCT images.

Indexed as

artificial intelligencebony labyrinthcomputed tomographyossicular chainsemi-supervised learningthree-dimensional reconstruction

Identifiers

PMID41059249
PMCPMC12429245

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.