Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Development of a novel forensic age estimation strategy for aged blood samples by combining piRNA and miRNA markers

In forensic investigations, age estimation is vital for determining whether a suspect is under or over the legally defined adult age. With breakthroughs in RNA sequencing technology, small noncoding RNAs have provided new ways to solve problems related to the age estimation of trace or aged samples, owing to their small molecular weight and better stability. In our previous study, we had applied miRNAs for the age estimation of bloodstains; however, further improvement of the existing model is needed. PIWI-interacting RNAs (PiRNAs), which are 24-32 nt noncoding small RNA molecules involved in the PIWI-piRNA pathway, play an important role in the aging process. In this study, we explored the possibility of simultaneously analyzing piRNAs and miRNAs for better age estimation purpose. Through massively parallel sequencing, five age-related piRNAs were identified in blood samples that had been stored for eight years. Further real-time PCR analysis revealed that two piRNAs (piR-000753 and piR-020548) showed relatively higher efficiency in age estimation. Additionally, two age-related miRNAs (miR-324-3p and miR-330-5p) were used to build the estimation model. Among all algorithms tested, gradient boosting showed the lowest mean absolute error (MAE) and root mean square error (RMSE) values (3.171 and 4.403 years, respectively) for the validation dataset (n = 110). The errors of the model were less than 5 years and 10 years for 81.82% and 96.36% of the samples, respectively. The results suggest that the combined use of piRNA and miRNA markers may increase the accuracy of age estimation, and our new model has great potential for application in forensic casework.

The distal femoral epiphysis in forensic age diagnostics: studies on the evaluation of the ossification process by means of T1- and PD/T2-weighted magnetic resonance imaging

The age of majority, which corresponds to the age of 18 years in most European countries, plays a crucial role for a large number of legal decisions. Accordingly, an increasing number of requests by authorities to forensic age estimation experts comprise the question of whether the age of 18 years has been reached by an individual. In recent years, novel study data suggested that magnetic resonance imaging (MRI) of the knee might likewise allow for the determination of majority beyond reasonable doubt. However, the data basis, especially concerning the distal femoral epiphysis (DFE), is still poor. For this reason, 392 routine MRI cases of the knee (204 males and 188 females of a Western Caucasian population, aged between 12 and 25 years) were retrospectively analyzed. T1-weighted and water-selective fat-saturated PD/T2-weighted sequences, generated at 1.5 and 3.0 T clinical MR scanners, were available. Ossification stages of the DFE were determined by means of the classification system by Vieth et al. (Eur Radiol 2018; 28:3255-3262). Both the intra-observer agreement and inter-observer agreement were found to be "very good" (κ = 0.899 and κ = 0.830). The present study confirmed that MRI of the DFE is suitable to determine majority in both sexes when stage 6 is present as the study revealed minimum ages above the age of 18 years for this stage (20.40 years in males and 20.60 years in females). Accordingly, the data represent a strong support for the so far existing database. Hence, the investigation of the knee using routine MRI appears to become a realistic alternative for forensic age estimation practice in the near future.