## 4 Discussion
This may be due to the fact that more features can be obtained from each subject using 3D images rather than using 2D images and that more features can be obtained from each subject per age than in the present study because more subjects were included in the study. The reason for this may be that the number of features that can be obtained from each age group is larger than that of the present study. Conversely, [PERSON] et al. [30] reported an age estimation method using 3D brain MR images for 1101 subjects aged 20-80 years. In this report, the correlation coefficient was 0.96, which was equivalent to that of the present study, although the MAE was 3.67 years, which was approximately 1.6 years smaller than that of the present study. Although the differences in MAE may be attributed to the differences in the number of subjects and the image format, an overview of the estimated age against CA suggests that this method already has the performance to show the same trend as the previous studies. The abovementioned comparison with previous studies suggests that the estimation accuracy of the present method will be further improved with an increase in the number of data and similar analysis using 3D-CNN. Next, as for the analysis of the significant region, the accuracy of age estimation significantly decreased before and after the deletion of the corpus callosum region in the overall evaluation of this method. This result suggests that the corpus callosum is a useful region for age estimation, and the results show a similar trend to those obtained in previous studies. The corpus callosum connects the left and right cerebral hemispheres, which are separated by the median longitudinal fissure, and therefore plays a role in the transmission of information between the two hemispheres, as it is composed of more than 190 million axons. Cognitive functions are affected by cerebral corpus callosum dysplasia and cerebral corpus callosum disconnection [36; 37], and considering this, it can be said that the corpus callosum plays a role in brain functions corresponding to higher brain dysfunction and cognitive dysfunction. Thus, taking into account that brain atrophy and cognitive decline progress with aging, it is considered that some morphological changes have occurred in the corpus callosum region due to aging and that the AI focused on these changes to estimate the age of the subjects. However, although we focused only on the corpus callosum region in this study, we cannot deny that other regions may have had a significant impact on age estimation. Therefore, the influence of other regions on age estimation should be examined by using heat mapping techniques, such as Grad-CAM [38] and occlusion sensitivity [39]. Finally, regarding the accuracy of age estimation between Male_Model and Female_Model, no statistically significant difference in estimation accuracy was found in 17 of 25 cases in this study, and significant differences were found in eight cases. Therefore, since no statistically significant differences were found overall, the results suggest the possibility of data collection methods that focus on age distribution regardless of sex in the field of research on age estimation by combining deep learning techniques and brain MR images. For example, the results indicate that the brain MR images of a \"42-year-old man\" and a \"42-year-old woman\" can be treated as data of a 42-year-old person. Therefore, this result supports the fact that it is possible to collect data more easily without falling into the data shortage caused by the consideration of the sex ratio at the time of data collection, as in the present study. However, in eight of 25 cases (32%), there was a significant difference in the accuracy of age estimation. There are various theories regarding anatomical differences in brain structure.