Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue

This study tackles the challenge of automatically estimating age from pelvis radiographs. Furthermore, we aim to develop a methodology for applying artificial intelligence to classify or regress medical imagery data. Our dataset comprises 684 pelvis X-ray images of patients, each accompanied by annotations and masks for various regions of interest (e.g., the femur shaft). Radiomic features, e.g., the co-occurrence matrix, were computed to characterize the image content. We assessed statistical analysis, machine learning, and deep learning methods for their effectiveness in this task. Correlation analysis indicated that using certain features in specific regions of interest is promising for accurate age estimation. Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. Automatically selecting radiomic features for machine learning models achieves a MAE of 7.99, whereas utilizing well-known convolutional architectures on the original image results in a system efficacy of 7.96. The use of artificial intelligence in medical data analysis produces comparable outcomes; however, when dealing with a large number of descriptors, selecting the most optimal ones through statistical analysis enables the identification of the best solution quickly.