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, 14.4-19.3 years) were prospectively evaluated for epiphyseal growth plate closure of the right knee by recordings of two-dimensional US volumes and a high-resolution T1-weighted MRI sequence. The degree of epiphyseal growth plate closure was rated independently by two readers for each method using a modality specific three-point scale that differentiates between an open physis (S1), a partially closed physis (S2), and a closed physis (S3). RESULTS: The inter-rater agreement was high for the US (Cohen's kappa (CK): femur 95.2%, tibia 81.3%, fibula 86.3%) and the MRI method (CK: femur 70.2%, tibia 90.8%, fibula 79.8%). The degree of growth plate closure associated positively with advancing age. The US system showed a clearer separation of median ages with lower overlap than the MRI system. Open growth plates on minors (< S3 on femur and tibia) were identified by US with higher sensitivity (1.0 vs. 0.7) and slightly lower specificity (0.7 vs. 0.85) compared with MRI. The examination time was substantially shorter on US than on MRI (2.65 ± 0.91 min vs. 24.72 ± 2.72 min; p < 0.001). CONCLUSIONS: The US method for evaluation of growth plate closure of the knee can reliably assign male individuals to different ossification stages and identifies minors with high accuracy. More studies with larger numbers are needed to further evaluate this method. KEY POINTS: • US is feasible to determine the degree of epiphyseal growth plate closure of the knee, shows a high degree of reliability, and is comparable to MRI. • US of the knee can detect open growth plates on male minors with high accuracy. • US of the knee may be used as a fast, non-invasive imaging tool for forensic age estimation to identify male minors.

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. The study includes 40 male subjects from northern Germany aged 14 to 21 years. Three MRI examinations were evenly acquired within 2 years for each subject. As a first evaluation, a three-stage system was used to assess the ossification status of the knee (I:"open", II:"partially ossified", III:"fully ossified"). Three raters assessed the growth plate of the distal femur, proximal tibia, and proximal fibula based on central 2D slices. A good inter-rater agreement was attained (κ = 0.84). All subjects younger than 18 years were rated as stage I and had a cumulative knee score (SKJ) ≤ 5. Based on the follow-up datasets, new parameters quantifying the intra-individual ossification process were calculated. The results of this follow-up analysis show a different start, end, and speed of each growth plate's maturation as well as an ossification peak for individuals at the age of 16. The generated MRI database provides new insights into the ossification process over time and serves as a basis for further evaluations of age estimation methods.

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. An approach which enables an automated detection and analysis of the bone structures may be necessary to handle large datasets. The purpose of this study was to develop a fully automatic 2D knee segmentation based on 3D MR images using convolutional neural networks. A total of 76 datasets were available and divided into a training set (74%), a validation set (13%) and a test set (13%). Multiple preprocessing steps were applied to correct image intensity values and to reduce the image size. Image augmentation was employed to virtually increase the dataset size for training. The proposed architecture for the segmentation task resembles the encoder-decoder model type used for the U-Net. The trained network achieved a dice similarity coefficient score of 98% compared to the manual segmentations and an intersection over union of 96%. The precision and recall of the model were balanced, and the error was only 1.2%. No overfitting was observed during training. As a proof of concept, the predicted segmentations were used for the age estimation of 145 subjects. Initial results show the potential of this approach attaining a mean absolute error of 0.48 ± 0.32 years for a test set of 14 subjects. The proposed automated segmentation can contribute to faster, reproducible and potentially more reliable age estimation in the future.