Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information
Abnormalities in the ossicular chain, a key middle-ear component that is crucial for sound transmission, can lead to conductive hearing loss; reconstruction offers an effective treatment. Accurate preoperative ossicular-chain measurements are essential for creating prostheses; however, current metho...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Cyborg and Bionic Systems |
| Online Access: | https://spj.science.org/doi/10.34133/cbsystems.0305 |
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| author | Mengshi Zhang Yufan Zhang Sihui Guo Xiaoguang Li Li Zhuo Yuxue Ren Wei Chen Yili Feng Ruowei Tang Han Lv Pengfei Zhao Zhenchang Wang Hongxia Yin |
| author_facet | Mengshi Zhang Yufan Zhang Sihui Guo Xiaoguang Li Li Zhuo Yuxue Ren Wei Chen Yili Feng Ruowei Tang Han Lv Pengfei Zhao Zhenchang Wang Hongxia Yin |
| author_sort | Mengshi Zhang |
| collection | DOAJ |
| description | Abnormalities in the ossicular chain, a key middle-ear component that is crucial for sound transmission, can lead to conductive hearing loss; reconstruction offers an effective treatment. Accurate preoperative ossicular-chain measurements are essential for creating prostheses; however, current methods rely on cadaver studies or manual measurements from 2-dimensional images, which are time-intensive and laborious and depend heavily on radiologist expertise. To improve efficiency, we aimed to develop a systematic approach for automated ossicular-chain segmentation and measurement using ultra-high-resolution computed tomography (U-HRCT). One hundred forty patients (226 ears) with normal ear anatomy underwent U-HRCT. Twelve parameters were defined to measure ossicular-chain components. Automated measurements based on automated segmentation of 226 ear images were verified through manual measurements. We analyzed variations by ear side, sex, and age group. Stapes analysis was limited by segmentation accuracy. Complete segmentation of the malleus, incus, and stapes was achieved in 47 ears. Automated measurements of 8 parameters showed no significant differences compared to manual measurements in 47 cases. Significant sex-based differences emerged in all parameters except stapes footplate length, incudostapedial joint angle, and stapes volume (P = 0.205, P = 0.560, and P = 0.170, respectively). Notable side-specific differences were observed in female incus height and male malleus volume (P = 0.017 and P = 0.037, respectively). No statistically significant differences were found in other parameters across different age groups, except for malleus and incus volumes (P = 0.015 and P = 0.031). The proposed algorithm effectively automated ossicular-chain segmentation and measurement, establishing a normative range for ossicular parameters and providing a valuable reference for detecting abnormalities. |
| format | Article |
| id | doaj-art-8605d6b6374d4a809d8ad5be1b60c349 |
| institution | Kabale University |
| issn | 2692-7632 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Cyborg and Bionic Systems |
| spelling | doaj-art-8605d6b6374d4a809d8ad5be1b60c3492025-08-20T03:28:43ZengAmerican Association for the Advancement of Science (AAAS)Cyborg and Bionic Systems2692-76322025-01-01610.34133/cbsystems.0305Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric InformationMengshi Zhang0Yufan Zhang1Sihui Guo2Xiaoguang Li3Li Zhuo4Yuxue Ren5Wei Chen6Yili Feng7Ruowei Tang8Han Lv9Pengfei Zhao10Zhenchang Wang11Hongxia Yin12Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100048, China.Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100048, China.Department of Medical Engineering, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.Abnormalities in the ossicular chain, a key middle-ear component that is crucial for sound transmission, can lead to conductive hearing loss; reconstruction offers an effective treatment. Accurate preoperative ossicular-chain measurements are essential for creating prostheses; however, current methods rely on cadaver studies or manual measurements from 2-dimensional images, which are time-intensive and laborious and depend heavily on radiologist expertise. To improve efficiency, we aimed to develop a systematic approach for automated ossicular-chain segmentation and measurement using ultra-high-resolution computed tomography (U-HRCT). One hundred forty patients (226 ears) with normal ear anatomy underwent U-HRCT. Twelve parameters were defined to measure ossicular-chain components. Automated measurements based on automated segmentation of 226 ear images were verified through manual measurements. We analyzed variations by ear side, sex, and age group. Stapes analysis was limited by segmentation accuracy. Complete segmentation of the malleus, incus, and stapes was achieved in 47 ears. Automated measurements of 8 parameters showed no significant differences compared to manual measurements in 47 cases. Significant sex-based differences emerged in all parameters except stapes footplate length, incudostapedial joint angle, and stapes volume (P = 0.205, P = 0.560, and P = 0.170, respectively). Notable side-specific differences were observed in female incus height and male malleus volume (P = 0.017 and P = 0.037, respectively). No statistically significant differences were found in other parameters across different age groups, except for malleus and incus volumes (P = 0.015 and P = 0.031). The proposed algorithm effectively automated ossicular-chain segmentation and measurement, establishing a normative range for ossicular parameters and providing a valuable reference for detecting abnormalities.https://spj.science.org/doi/10.34133/cbsystems.0305 |
| spellingShingle | Mengshi Zhang Yufan Zhang Sihui Guo Xiaoguang Li Li Zhuo Yuxue Ren Wei Chen Yili Feng Ruowei Tang Han Lv Pengfei Zhao Zhenchang Wang Hongxia Yin Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information Cyborg and Bionic Systems |
| title | Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information |
| title_full | Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information |
| title_fullStr | Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information |
| title_full_unstemmed | Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information |
| title_short | Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information |
| title_sort | exploration of automated measurement for ossicular chains based on 3 dimensional geometric information |
| url | https://spj.science.org/doi/10.34133/cbsystems.0305 |
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