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Bone age is one of the most important evaluation indexes for the growth and development of children and adolescents. The bone age assessment method based on deep learning generally uses the whole left wrist X-ray film or some regions of interest in the left wrist X-ray film. Based on the entire X-ray film, the intelligent evaluation process is simple, but the accuracy is low. Although intelligent evaluation based on regions of interest has high accuracy, it requires prior knowledge and the process is complex. To solve the above problems, this paper proposes a multi-region combined method for bone age assessment. A small number of regions of interest in wrist bone X-ray films are extracted, and then the whole X-ray film and these regions of interest were used to evaluate the bone age. The experiment uses the improved Inception-ResNet-V2 convolutional neural network. The results show that compared with other bone age assessment studies on the open data set published by the North American radiological Association, this method can obtain higher accuracy of bone age assessment, with an average absolute error of 7.11 months. This method improves the efficiency and accuracy of bone age assessment while simplifying the assessment process.

Research on an intelligent evaluation method of bone age based on multi-region combination