Table 1.
Overview of machine learning algorithms and applications used in musculoskeletal ultrasound imaging
| Algorithm | Advantage | Limitation | Example application in musculoskeletal ultrasonography |
|---|---|---|---|
| Logistic regression | Provides probabilistic interpretation of model parameters | Only used to predict discrete function | - |
| Quick model update for incorporating new data | Sensitive to outliers | ||
| K-nearest neighbors | Nonparametric model | Time-consuming and computationally expensive | Nerve identification [10] |
| Used both for classification and regression problems | Number of neighbors must be defined in advance | ||
| Low interpretability | |||
| Naïve Bayes | Suitable for relatively small datasets | Classes must be mutually exclusive | - |
| Handles both binary and multi-class classification problems | Presence of dependency between attributes results in loss of accuracy | ||
| Fast application and high computational efficiency | Assumptions such as the normal distribution might be invalid | ||
| Support vector machines | Good prediction performance in different tasks | Have "black box" characteristics | Lumbar spine classification [11] |
| Can handle multiple feature spaces | Sensitive to manual parameter tuning and kernel choice | Synovitis grading [12] | |
| Nerve identification [10] | |||
| Decision trees | Perform in datasets with large number of features | Only axis-aligned rectangle splits. | Nerve identification [10] |
| Few parameter tuning | Inadequate for regression and continuous value prediction problems | ||
| High representational power and easy to interpret | Mistake in higher labels cause errors in subtrees | ||
| Random forest | Provide estimates of variable or attribute importance in the classification | Complex and computationally expensive | Myositis classification [13] |
| Ensemble-based classifications shows relatively good performance | Number of base classifiers needs to be defined | Hip 2-D US adequacy classification [14] | |
| Overfitting has been observed for noisy data | |||
| Neural networks | Direct image processing | Have "black box" characteristics | Nerve identification [10] |
| Can map complex nonlinear relationships between dependent and independent variables | Have to fine-tune many parameters | ||
| Require a large well-annotated dataset to achieve good performance | |||
| K-means | Can process large datasets | Number of clusters must be defined | Nerve localization [15] |
| Algorithm that is simple to understand and implement |