Impact of Ensemble Learning in the Assessment of Skeletal Maturity

Abstract

The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

Machine Learning in Orthopedics: A Literature Review

“…Other ensemble methods were applied to the problem of skeletal maturity assessment (Cunha et al, 2014 ). Bootstrap aggregating (bagging) was used to obtain an aggregated predictor, beginning from using bootstrap replicates of the training sets as multiple predictors.…”

“…The best approaches were LDA (witn a ROC of 0.82) and the weighted k-NN (with a ROC of 0.81). The same approach was one of the best performing ones in the task of skeletal maturity assessment among over 20 ML methods that were tested in the same study (Cunha et al, 2014 , see section 4.1.6.1 for details on this research).…”

Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

“…The utilization of regression to identify bone age has been utilized by a few analysts [23][24][25]. Furthermore, the utilization of random forest [26], K-NN [27], SVM [28][29][30], ANN [24,31,32], and Fuzzy Neural system [33] has been done by a few authors. The utilization of deep learning models also has been contributed by certain scientists to estimate the bone age [6,[34][35][36]54].…”

An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism

“…In one of the paper Cunha et al [28] implemented a technique in order to improve bone age prediction and they named the technique as ensemble technique. The aim of this technique is to extract various features from joints of fingers and also these feature descriptors are merged with the ensemble technique in prediction of bone age.…”