Bone Age Prediction with AI Models
|© 2023 by IJCTT Journal|
|Year of Publication : 2023|
|Authors : Chi-Chang Chen, Yu-Xian Chou|
|DOI : 10.14445/22312803/IJCTT-V71I2P104|
How to Cite?
Chi-Chang Chen, Yu-Xian Chou, "Bone Age Prediction with AI Models," International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 19-24, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I2P104
Artificial intelligence (AI) models have been developed to assist in the process of bone age prediction by automating the assessment of radiographic images. These models use machine learning algorithms to learn from a dataset of previously assessed images and can then make predictions about the bone age of new images with high accuracy. In this paper, we use four AI models, namely, VGG16, ResNet50, ResNet152, and Xception, to automatically predict the bone ages of X-ray images from the Radiological Society of North America (RSNA). According to our experiments, Xception got better results than the others three models. Both the mean absolute error(mae) and median absolute error of Xception was 7.21 months. These AI models have the potential to improve the accuracy, consistency, and efficiency of bone age prediction. However, there are also limitations and challenges to using AI models for bone age prediction, such as the need for large and diverse training sets and robust validation and testing. Further research and development are needed to address these challenges and limitations to ensure that the AI models for bone age prediction are reliable and accurate in real-world settings.
Bone Age Prediction, Machine Learning, Deep Learning, Medical image processing.
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