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