Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence
Book cover pp 29–37Cite as Part of the Abstract Talus fractures keep on presenting to a difficult and generally experienced gathering of injuries. This published report shows that not all the current bone implants are the ideal counterpart for the specific population. Along these lines,...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/35929/1/17.Prediction%20of%20Malaysian%20Talus%20Bone%20Morphology%20Using%20Artificial%20Intelligence.pdf |
| _version_ | 1796995430136938496 |
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| author | Rosdi, Daud Nurazlina, Sulaeman Mas Ayu, Hassan Arman Shah, Abdullah |
| author_facet | Rosdi, Daud Nurazlina, Sulaeman Mas Ayu, Hassan Arman Shah, Abdullah |
| author_sort | Rosdi, Daud |
| collection | UMP |
| description | Book cover
pp 29–37Cite as
Part of the
Abstract
Talus fractures keep on presenting to a difficult and generally experienced gathering of injuries. This published report shows that not all the current bone implants are the ideal counterpart for the specific population. Along these lines, this investigation received a three-dimensional (3D) estimation way to deal with given exact information to the anatomical morphology of talus bone. Seventy-four Malaysian healthy subjects experienced computerized tomography (CT) arthrography. 3D computerized talar models were generated and three morphological boundaries predicted through Mimics and Solidworks software. Sagittal Talar radius (STRa), Throchlea Tali length (TTL), Talar Anterior width (TaAW) are the most part chosen. Information investigation was directed by determination of information test through Matlab programming. In this way, the information was obtained dependent on the artificial intelligence (AI) forecast of the talus bone morphometric. While, the AI strategy demonstrated a more noteworthy limit of forecast in regards to the low level of mistake and high correlative qualities since the average percentage errors of the predicted talus bone morphology parameters are around 10% which 11.3% for STRa, 12.95% for TaAW, and 9.45% for TTL. AI is an exceptionally exact prescient technique and can be utilized as helping instruments in developing bone implant specifically for Malaysian patient and for Asian patient in general. |
| first_indexed | 2024-03-06T13:02:14Z |
| format | Book Chapter |
| id | UMPir35929 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:02:14Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir359292022-12-15T02:40:27Z http://umpir.ump.edu.my/id/eprint/35929/ Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence Rosdi, Daud Nurazlina, Sulaeman Mas Ayu, Hassan Arman Shah, Abdullah TJ Mechanical engineering and machinery TS Manufactures Book cover pp 29–37Cite as Part of the Abstract Talus fractures keep on presenting to a difficult and generally experienced gathering of injuries. This published report shows that not all the current bone implants are the ideal counterpart for the specific population. Along these lines, this investigation received a three-dimensional (3D) estimation way to deal with given exact information to the anatomical morphology of talus bone. Seventy-four Malaysian healthy subjects experienced computerized tomography (CT) arthrography. 3D computerized talar models were generated and three morphological boundaries predicted through Mimics and Solidworks software. Sagittal Talar radius (STRa), Throchlea Tali length (TTL), Talar Anterior width (TaAW) are the most part chosen. Information investigation was directed by determination of information test through Matlab programming. In this way, the information was obtained dependent on the artificial intelligence (AI) forecast of the talus bone morphometric. While, the AI strategy demonstrated a more noteworthy limit of forecast in regards to the low level of mistake and high correlative qualities since the average percentage errors of the predicted talus bone morphology parameters are around 10% which 11.3% for STRa, 12.95% for TaAW, and 9.45% for TTL. AI is an exceptionally exact prescient technique and can be utilized as helping instruments in developing bone implant specifically for Malaysian patient and for Asian patient in general. Springer Science and Business Media Deutschland GmbH 2022-03-12 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/35929/1/17.Prediction%20of%20Malaysian%20Talus%20Bone%20Morphology%20Using%20Artificial%20Intelligence.pdf Rosdi, Daud and Nurazlina, Sulaeman and Mas Ayu, Hassan and Arman Shah, Abdullah (2022) Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence. In: Advanced Materials and Engineering Technologies. Advanced Structured Materials book series (STRUCTMAT), 162 . Springer Science and Business Media Deutschland GmbH, Springer Link Malaysia, pp. 29-37. ISBN 978-3-030-92963-3 (Printed); 978-3-030-92964-0(Online) https://doi.org/10.1007/978-3-030-92964-0_4 https://doi.org/10.1007/978-3-030-92964-0_4 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Rosdi, Daud Nurazlina, Sulaeman Mas Ayu, Hassan Arman Shah, Abdullah Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title | Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title_full | Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title_fullStr | Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title_full_unstemmed | Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title_short | Prediction of Malaysian Talus Bone Morphology Using Artificial Intelligence |
| title_sort | prediction of malaysian talus bone morphology using artificial intelligence |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/35929/1/17.Prediction%20of%20Malaysian%20Talus%20Bone%20Morphology%20Using%20Artificial%20Intelligence.pdf |
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