Artificial intelligence in orthopedics: three strategies for deep learning with orthopedic specific imaging

Excerpt

Artificial intelligence (AI) has achieved remarkable results throughout society, including within the field of medicine. As the techniques advance, it is not uncommon for AI to outperform clinicians under certain conditions [2]. A branch of AI, known as machine learning, denotes the ability of a machine to identify relationships between data without explicit criteria. This relationship identification typically improves with increasing experience and data and allows algorithms to model relationships which may otherwise be too complex for standard statistical methods. Deep learning is a field of machine learning that refers to a model with an artificial neural network structure and mimics the human brain's neural connections (Fig. 1). The most important determinant of conventional machine learning algorithm performance (other than data quality and quantity) is appropriate selection of features. If the feature selection process is executed appropriately, it is possible to achieve sufficiently effective performance regardless of type of model used. On the other hand, if feature selection is unsuccessful, it is difficult to achieve adequate performance, irrespective of the popularity or purported capacity of the candidate algorithm. Currently, there is no gold standard for the process of feature selection. Thus, there is still a need for careful methodology that outlines the technical and medical knowledge when utilizing traditional machine learning algorithms. Conversely, deep learning has the advantage of end-to-end analysis using input data without the feature selection process. It provides the advantage of not having to rely strictly on feature selection as it utilizes all available parameters. However, deep learning also has an entry barrier that requires data preparation for training. In addition, securing a high-performance graphics processing unit (GPU) for an efficient experiment is important as model training times and costs can often become increasingly burdensome [3].

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