Abstract
In this study we examined the lower limb fracture in children and classified the healing time using supervised and unsupervised artificial neural network (ANN). Radiographs of long bones from 2009 to 2011 of lower limb fractures involving the femur, tibia and fibula from children ages 0 to 13 years, with ages recorded from the date and time of initial injury was obtained from the pediatric orthopedic unit in University Malaya Medical Centre. ANNs was developed using the following input: type of fracture, angulation of the fracture, displacement of the fracture, contact area of the fracture and age. Fracture healing time was classified into two classes that is less than 12 weeks which represent normal healing time in lower limb fractures and more than 12 weeks which could indicate a delayed union. This research was designed to evaluate the classification accuracy of two ANN methods (SOM, and MLP) on pediatric fracture healing. Standard feed-forward, back-propagation neural network with three layers was used in this study. The less sensitive variables were eliminated using the backward elimination method, and the ANN network was retrained again with minimum variables. Accuracy rate, area under the curve (AUC), and root mean square errors (RMSE) are the main criteria used to evaluate the ANN model results. We found that the best ANN model results was obtained when all input variables were used with overall accuracy percentage of 80%, with RMSE value of 0.34, and AUC value of 0.8. We concluded here that the ANN model in this study can be used to classify pediatric fracture healing time, however extra efforts are required to adapt the ANN model well by using its full potential features to improve the ANN performance especially in the pediatric orthopedic application.
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References
Staheli, L.: Fundamentals of Pediatric Orthopedics, 3rd edn. Lippincott Williams and Wilkins, Pennsylvania (2003)
University of Rochester Medical Center, May 27, 2015. http://www.urmc.rochester.edu/encyclopedia/content.aspx?ContentTypeID=90&ContentID=P02760
Patton, D.F.: Fractures and orthopaedics. Churchill Livingstone, Edinburgh (1992)
Hobbs, C.J.: Fractures. Br. Med. J. 298, 1015–1018 (1989)
Kempe, R.S., Silverman, F.N., Steele, B.F., Droegemueller, W., Silver, H.K.: The battered child syndrome. Am. J. Med. Sci. 181(1), 17–24 (1962)
Shi, L., Wang, X.C., Wang, Y.S.: Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Brazilian Journal of Medical and Biological Research 46, 993–999 (2013). doi:10.1590/1414-431X20132948
Kukar, M., Kononenko, I., Silvester, T.: Machine learning in prognosis of the femoral neck fracture recovery. Artif. Intell. Med. 8(5), 431–451 (1996)
Taylor, R.J., Taylor, A.D., Smyth, J.V.: Using an artificial neural network to predict healing times and risk factors for venous leg ulcers. J. Wound Care 11(3), 101–105 (2002)
Sharpe, P.K., Caleb-Solly, P.: Self organising maps for the investigation of clinical data: A case study. Neural Computing and Applications 7(1), 65–70 (1998). ISSN 0941-0643, http://eprints.uwe.ac.uk/19201
Ogden, J.A.: Injury to the immature skeleton. In: Touloukian, R. (ed.) Pediatric Trauma, 2nd edn. John Wiley & Sons, New York (1990)
Ogden, J.A.: Skeletal Injury in the Child, 2nd edn. WB Saunders, Philadelphia (1990)
Staheli, L.: Practice of Pediatric Orthopedics, 2nd edn. Lippincott Williams & Wilkins, Philadelphia (2006)
Natick, MA, R20013, MathWorks
Meiller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993)
Smith, A.E., Mason, A.K.: Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist 42(2), 137–161 (1997)
Kohonen, T.: Self- Organization and Associative Memory, 3rd edn. Springer-Verlag (1989)
Hollmén, J.: Process modeling using the selforganizing map, Master’s thesis, Department of Computer Science, Helsinki University of Technology (1996)
McKibbin, B.: The biology of fracture healing in long bone. J. Bone Joint Surg. 60B, 150–162 (1978)
Ryöppy, S.: Injuries of the growing skeleton. Ann. Chir. Gynaecol. Fenn. 61, 3–10 (1972)
Tseng, W.-J., Hung, L.-W., Shieh, J.-S., Abbod, M.F., Lin, J.: Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study. BMC Musculoskeletal Disorders 14, 207 (2013)
Milan, Z.M., Markovic, M.M., Adreja, B.S.: A performance analysis of the multilayer perceptron in limited training data set conditions. IEEE (1997)
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Malek, S. et al. (2016). A Primary Study on Application of Artificial Neural Network in Classification of Pediatric Fracture Healing Time of the Lower Limb. In: Saberi Mohamad, M., Rocha, M., Fdez-Riverola, F., Domínguez Mayo, F., De Paz, J. (eds) 10th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2016. Advances in Intelligent Systems and Computing, vol 477. Springer, Cham. https://doi.org/10.1007/978-3-319-40126-3_3
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DOI: https://doi.org/10.1007/978-3-319-40126-3_3
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