Automated age estimation of young individuals based on 3D knee MRI using deep learning.

Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects.

Forensic age assessment of the knee: proposal of a new classification system using two-dimensional ultrasound volumes and comparison to MRI.

Objectives: To assess epiphyseal growth plate closure of the knee for forensic age estimation using an ultrasound (US)-based method and to compare the findings with MRI.

Methods: Thirty-three healthy male individuals (age, 14.4-19.

Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks.

Age assessment is used to estimate the chronological age of an individual who lacks legal documentation. Recent studies indicate that the ossification degree of the growth plates in the knee joint correlates with chronological age of adolescents and young adults. To verify this hypothesis, a high number of datasets need to be analysed.

A 2-year follow-up MRI study for the evaluation of an age estimation method based on knee bone development.

Age estimation is an actual topic in the area of forensic medicine with a special focus on the age limits of 16 and 18 years. Current research on this topic relies on retrospective data of inhomogeneous populations relating to sex, age range, and socioeconomic status. In this work, we present a 2-year follow-up study for the evaluation of an age estimation method on a prospective magnetic resonance imaging (MRI) knee data collective of a homogeneous population.