DEEP LEARNING MODELING USING T1-WEIGHTED IMAGES IN MAGNETIC RESONANCE IMAGING OF THE KNEE JOINTS AND ITS USE IN AGE ESTIMATION OF LIVING BODIES

Abstract

Objective To discuss deep learning modeling using T1-weighted images (T1WI) in magnetic resonance imaging (MRI) of the knee joints and its use in age estimation of adolescents. Methods The T1WI of the knee joints were collected from 1 212 male patients aged 10-18 years who were admitted to The Affiliated Hospital of Qingdao University from January 2015 to December 2021 (internal data set) and 341 male patients of the same ages who were admitted to Qingdao Municipal Hospital during the same period (external data set). After labeling and image segmentation of the epiphyseal plates of the distal femurs and proximal tibiae, the internal data set was divided into training group (971 cases) and validation group (241 cases) at a ratio of 8∶2 according to their age groups using a random number table for modeling, and the external data set (test group) was used for model evaluation. The performance of the model was tested and validated based on accuracy, precision, recall rate, sensitivity, and specificity. Results The accuracy, precision, recall rate, specificity, and sensitivity of the validation group were 85.713%, 84.732%, 85.713%, 97.729%, and 85.713%, respectively; the same indicators of the test group were 82.578%, 83.145%, 82.578%, 97.442%, and 82.578%, respectively. There were no significant differences in the above indicators between the validation group and the test group (P>0.05). Conclusion A deep learning model based on the T1WI of the knee joints is successfully constructed, and it can be used for age estimation of adolescents aged 10-18 years.

Keywords

• knee joint|magnetic resonance imaging|forensic medicine|deep learning|age determination by skeleton|adolescent