A Primary Study on Application of Artificial Neural Network in Classification of Pediatric Fracture Healing Time of the Lower Limb

@inproceedings{Malek2016APS,
  title={A Primary Study on Application of Artificial Neural Network in Classification of Pediatric Fracture Healing Time of the Lower Limb},
  author={Sorayya Malek and Roshan Gunalan and S. Y. Kedija and C. F. Lau and Mogeeb A. A. Mosleh and Pozi Milow and H. Amber and Aik Saw},
  booktitle={PACBB},
  year={2016}
}
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… 

MACHINE LEARNING TECHNIQUE IN APPLICATION AND COMPARISON IN PEDIATRIC FRACTURE HEALING TIME KEDIJA FACULTY OF SCIENCE UNIVERSITY OF MALAYA

It is concluded that RF, Decision Tree, SVM, ANN and SOM techniques can be used to assist in analysis of the healing time efficiently and contact area percentage of fracture, type of fractures, number of fractured bone and age as important variables in explaining the fracture healing pattern.

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools for paediatric fracture assessment on imaging may generalise to a wider paediatric population.

How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?

This book chapter offers a review of literature on the use of AI, ML, and ANN in the study of bone mechanics research and introduces AI and ML and elaborates on the main features of ML algorithms such as learning paradigms, subtypes, main ideas with examples, performance metrics, training algorithms, and training datasets.

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