A machine-learning approach using pubic CT based on radiomics to estimate adult ages

Adult skeletal age estimation is an active research field. To evaluate the performance of a pubic CT radiomics-based machine learning model for estimating age, we established a multiple linear regression model based on radiomics and machine learning methods.

A total of 355 subjects were enrolled in this retrospective study from August 2016 to August 2021, and divided into a training cohort (N = 325) and a testing cohort (N = 30). Computerized texture analysis of the semi-automatically segmentation was performed and 107 texture features were extracted from the regions. Then we used univariate linear regression and multivariate stepwise regression to assess correlations of texture parameters with age. The most vital features were used to make the best predictive model. Eventually, the established radiomics model was tested with an additional 30 patients.

Clinical characteristics include age, sex, height, weight and BMI were not statistically significant different between training and testing cohort (p = 0.098-0.888). Through a multivariate regression analysis using stepwise regression, six texture parameters were found to have significant correlations with age. The regression formula estimating the age was constructed.

The radiomics model using machine learning is considered as a new approach forage estimation frompubic symphysis CT features.Digital osteology is obtained in a non-invasive way so that it can be an ideal collection for anthropological studies.