Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.

This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Novel NiCl2 Nanosheets Synthesized via Chemical Vapor Deposition with High Specific Energy for Thermal Battery.

Two-dimensional (2D) nanomaterials possessing a unique sheet structure, compared to correlative bulk materials, exhibit excellent properties, especially in the energy storage and energy conversion field. In this case, NiCl2 nanosheets with thicknesses of 2-8 nm are first prepared by a simple chemical vapor deposition method. For the Li-B/LiF-LiCl-LiBr/NiCl2 thermal battery, the specific energy of NiCl2 nanosheets increases from 510 W h kg-1 (NiCl2 rods) to 616 W h kg-1 at an operation temperature of 500 °C and a current density of 0.2 A cm-2. The 2D morphology and large numbers of defects not only improve the redox reaction rates and the lithium storage capacity, but also enhance the adsorption capacity with the flake-like binder MgO, which prolong the discharge time by suppressing the discharge product diffusion to the electrolyte. These results indicate that NiCl2 nanosheets have a great possibility to become a desirable candidate of cathode materials for assisting in the development of high energy output and provide a new way to restrain the immersion between the electrode and electrolyte.