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Deveploment of Novel Talus Implant Based on Artificial Neural Network Prediction of Talus Morphological Parameters

R., Daud and Suaidah, S. and Mohammed Rafiq, Abdul Kadir and Sudin, Izman and Mas Ayu, Hassan and Hanumantharao, Balaji Raghavendran and Tunku, Kamarul (2015) Deveploment of Novel Talus Implant Based on Artificial Neural Network Prediction of Talus Morphological Parameters. In: World Bio Summit & Expo 2015, 1-5 Nov 2015 , Dubai. . (Unpublished)

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The design philosophies of current talus implant focus too much on mechanical simplicity and usually based on certain population which tends to ignore the bony geometry difference between populations. Thus, the novel talus implant (NTI) for particular population was developed based on artificial neural network (ANN) prediction of talus morphometrics. By using Finite Element Method (FEM), numerical models that include mainly the talus bone and the talus implants are created to compare the performance of the NTI with the three different kind of current talus implant designs. The study demonstrates that not all current talus implant are perfect match for this particular population. While, the ANN method showed a greater capacity of prediction regarding on the low percentage of error and high correlative values with the measurements obtained through Computer Tomographic (CT) scan. ANN is highly accurate predictive methods and has the potential to be used as assisting tools in designing talus implant. For FEM results, only BOX and NTI exceeded the contact stress recommended for the superior articular surface compared to the others. The results also showed that the stress increased near the resected surface. Thus, it is agreed that excessive bone resection may not support the force at the ankle which consequently may contribute to early loosening and subsidence of the talus implant. It is concluded that the excessive bone resection can be avoided by perfectly match talus implant which only can be achieved by designing talus implant for a particular population.

Item Type: Conference or Workshop Item (Other)
Uncontrolled Keywords: Talus Morphometric; Artificial Neural Network; Finite Element Method
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 24 Nov 2015 02:18
Last Modified: 31 Jan 2018 06:58
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