Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters

Current ankle morphometric measurement tools involve the use of radiographic techniques which maybe rmacceptable to many ethical committees due to the radiation exposure to subjects. In the present study, we propose an alternative method of ankle morphometric measurement using neural network computa...

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Main Authors:	R., Daud, Mas Ayu, Hassan, Salwani, Mohd Salleh, Siti Haryani, Tomadi, Mohammed Rafiq, Abdul Kadir, Raghavendran, Hanumantharao Balaji, Tunku, Kamarul
Format:	Article
Language:	English
Published:	Medwell Journals 2017
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/20082/ http://umpir.ump.edu.my/id/eprint/20082/ http://umpir.ump.edu.my/id/eprint/20082/1/fkm-2017-masayu-Artificial%20Neural%20Network%20The%20Alternative%20Method.pdf

id	ump-20082
recordtype	eprints
spelling	ump-200822018-03-22T04:14:35Z http://umpir.ump.edu.my/id/eprint/20082/ Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters R., Daud Mas Ayu, Hassan Salwani, Mohd Salleh Siti Haryani, Tomadi Mohammed Rafiq, Abdul Kadir Raghavendran, Hanumantharao Balaji Tunku, Kamarul TJ Mechanical engineering and machinery Current ankle morphometric measurement tools involve the use of radiographic techniques which maybe rmacceptable to many ethical committees due to the radiation exposure to subjects. In the present study, we propose an alternative method of ankle morphometric measurement using neural network computational model based solely on existing data measurements and demographic information. The reliability and prediction power of this technique were examined and compared with the morphometric measurements of normal subjects using Computed Tomography (CT) scan measurements and Multiple Linear Regression (1.1LR) method of prediction. The Artificial Nemal Network (ANN) used in the present study was based on two-layer feed forward network. The network system included a hidden layer sigmoid transfer fllllction and a linear transfer fllllction in the output layer. For network training, standard levenberg-marquardt algorithm was used. The input used consisted of a set of demographic data (age, height and weight) while the output obtained from the analyses consisted of ankle morphometric measurements (Trochlea Tali Length (TTL) Talar Anterior Width (TaA W) Sagittal Radius of talar (SRTa) Tibia Length (TiL) Tibia Width (TiW) Widtli!LengthRatio of Talar (WLR Ta) and Widtli!Length Ratio of Tibia(WLRTi)). The applicability and accuracy of these alternative methods were evaluated by comparing the predicted values from our computational analysis with the normal CT values of 15 randomly selected volrmteers. Furthermore, our prediction values were also compared with the values predicted using the 1.1LR method. The ANN method showed a greater capacity of prediction and was folllld to estimate the ankle joint morphometric measurements with a low percentage of error and high correlative values with the measurements obtained through the use of CT scan. In addition, the ANN method was also noted to be better in predicting ankle measurements than the 1.1LR method as demonstrated by the lower average of standard deviations: SANN~ 1.35, SMLR ~ 2.20 for females and SANN~ 1.81, SMLR ~ 4.07 for males. The ANN method is potentially better alternative to predict ankle morphometric measurements than CT scan and 1.1LR methods. Medwell Journals 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/20082/1/fkm-2017-masayu-Artificial%20Neural%20Network%20The%20Alternative%20Method.pdf R., Daud and Mas Ayu, Hassan and Salwani, Mohd Salleh and Siti Haryani, Tomadi and Mohammed Rafiq, Abdul Kadir and Raghavendran, Hanumantharao Balaji and Tunku, Kamarul (2017) Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters. Journal of Engineering and Applied Sciences, 12 (10). pp. 2782-2787. ISSN 1816-949x (Print); 1818-7803 (Online) http://docsdrive.com/pdfs/medwelljournals/jeasci/2017/2782-2787.pdf
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery R., Daud Mas Ayu, Hassan Salwani, Mohd Salleh Siti Haryani, Tomadi Mohammed Rafiq, Abdul Kadir Raghavendran, Hanumantharao Balaji Tunku, Kamarul Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
description	Current ankle morphometric measurement tools involve the use of radiographic techniques which maybe rmacceptable to many ethical committees due to the radiation exposure to subjects. In the present study, we propose an alternative method of ankle morphometric measurement using neural network computational model based solely on existing data measurements and demographic information. The reliability and prediction power of this technique were examined and compared with the morphometric measurements of normal subjects using Computed Tomography (CT) scan measurements and Multiple Linear Regression (1.1LR) method of prediction. The Artificial Nemal Network (ANN) used in the present study was based on two-layer feed forward network. The network system included a hidden layer sigmoid transfer fllllction and a linear transfer fllllction in the output layer. For network training, standard levenberg-marquardt algorithm was used. The input used consisted of a set of demographic data (age, height and weight) while the output obtained from the analyses consisted of ankle morphometric measurements (Trochlea Tali Length (TTL) Talar Anterior Width (TaA W) Sagittal Radius of talar (SRTa) Tibia Length (TiL) Tibia Width (TiW) Widtli!LengthRatio of Talar (WLR Ta) and Widtli!Length Ratio of Tibia(WLRTi)). The applicability and accuracy of these alternative methods were evaluated by comparing the predicted values from our computational analysis with the normal CT values of 15 randomly selected volrmteers. Furthermore, our prediction values were also compared with the values predicted using the 1.1LR method. The ANN method showed a greater capacity of prediction and was folllld to estimate the ankle joint morphometric measurements with a low percentage of error and high correlative values with the measurements obtained through the use of CT scan. In addition, the ANN method was also noted to be better in predicting ankle measurements than the 1.1LR method as demonstrated by the lower average of standard deviations: SANN~ 1.35, SMLR ~ 2.20 for females and SANN~ 1.81, SMLR ~ 4.07 for males. The ANN method is potentially better alternative to predict ankle morphometric measurements than CT scan and 1.1LR methods.
format	Article
author	R., Daud Mas Ayu, Hassan Salwani, Mohd Salleh Siti Haryani, Tomadi Mohammed Rafiq, Abdul Kadir Raghavendran, Hanumantharao Balaji Tunku, Kamarul
author_facet	R., Daud Mas Ayu, Hassan Salwani, Mohd Salleh Siti Haryani, Tomadi Mohammed Rafiq, Abdul Kadir Raghavendran, Hanumantharao Balaji Tunku, Kamarul
author_sort	R., Daud
title	Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
title_short	Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
title_full	Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
title_fullStr	Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
title_full_unstemmed	Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
title_sort	artificial neural network: the alternative method to obtain the dimension of ankle bone parameters
publisher	Medwell Journals
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/20082/ http://umpir.ump.edu.my/id/eprint/20082/ http://umpir.ump.edu.my/id/eprint/20082/1/fkm-2017-masayu-Artificial%20Neural%20Network%20The%20Alternative%20Method.pdf
first_indexed	2023-09-18T22:28:46Z
last_indexed	2023-09-18T22:28:46Z
_version_	1777416144341172224

Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters

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