Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?

Abstract

The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers. Hand-wrist radiographs (HWRs) from 1067 individuals aged between 7and 18years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model1: only FD; model2: FD and Chapman sesamoid stage; model3: FD, age, and sex; model4: FD, Chapman sesamoid stage, age, and sex; model5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C5.0 decision tree classifier. All AI algorithms except for C5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model1 < model3 < model2 < model5 < model4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models1,2, and3 based on SVM, for model4 based on MLP, and for model5 based on C5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score. Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered agrowth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.