首页|Recep Tayyip Erdogan University Reports Findings in Artificial Intelligence (Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?)
Recep Tayyip Erdogan University Reports Findings in Artificial Intelligence (Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?)
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New research on Artificial Intelligence is the subject of a report. According to news reporting from Rize, Turkey, by NewsRx journalists, research stated, “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 7 and 18 years were included.” The news correspondents obtained a quote from the research from Recep Tayyip Erdogan University, “Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD; model 2: FD and Chapman sesamoid stage; model 3: FD, age, and sex; model 4: FD, Chapman sesamoid stage, age, and sex; model 5: 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 C 5.0 decision tree classifier. All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1<model 3<model 2<model 5<model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score.”
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