Bone Age Assessment: Traditional Machine Learning Methods and Their Applications

Bone age assessment is an important clinical task that involves determining the skeletal maturity of a child. Accurate bone age assessment is crucial for understanding growth patterns, diagnosing growth disorders, and monitoring treatment progress. Traditional machine learning methods have been widely used for bone age assessment, including regression and classification algorithms. Regression methods aim to predict the bone age of a child based on various features, such as chronological age, sex, and radiological features. Classification methods, on the other hand, aim to classify a child into a specific age group based on similar features.

Some of the popular traditional machine learning algorithms used for bone age assessment include linear regression, support vector regression, random forest regression, and k-nearest neighbor regression. These algorithms have been shown to provide accurate and reliable predictions, with some achieving mean absolute errors as low as 4 months.

References:

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