Adults Ischium Age Estimation Based on Deep Learning and 3D CT Reconstruction.

OBJECTIVES: To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. METHODS: The retrospective pelvic CT imaging data of 1 200 samples (600 males and 600 females) aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error (MAE) and root mean square error (RMSE) were used as primary indicators to evaluate the model. RESULTS: Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. CONCLUSIONS: The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age.

Sex Estimation of Han Adults in Western China Based on Three-Dimensional Cranial CT Reconstruction.

OBJECTIVES: To examine the reliability and accuracy of Walker's model for estimating the sex of Han adults in western China by using cranium three-dimensional (3D) CT reconstruction, and to study the suitable cranial sex estimation model for Han people in western China. METHODS: A total of 576 cranial CT 3D reconstructed images from Hanzhong Hospital in Shaanxi Province from 2017 to 2021 were collected. These images were divided into the experimental group with 486 samples and the validation group with 90 samples. Walker's model was used by observer 1 to estimate the sex of experimental group samples. The logistic function applicable to Han people in western China was corrected by observer 1. The 90 samples in the validation group were scored and substituted into the modified logistic function to complete the back substitution test by observer 1, 2 and 3. RESULTS: The accuracy of sex estimation of Han adults in western China was 63.2%-77.2% by applying Walker's model. The accuracy of modified logistic function was 82.9%. The accuracy of sex estimation through back substitution test by 3 observers was 75.6%-91.1%, with a Kappa value of 0.689 (P<0.05) for inter-observer consistency and 0.874 (P<0.05) for intra-observer consistency. CONCLUSIONS: There are great differences in bone characteristics among people from different regions. The modified logistic function can achieve higher accuracy in Han adults in western China.

Sex Estimation of Medial Aspect of the Ischiopubic Ramus in Adults Based on Deep Learning.

OBJECTIVES: To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population. METHODS: The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc. RESULTS: When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%. CONCLUSIONS: The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.

catena-Poly[[(1,10-phenanthroline-κN,N')cadmium(II)]-µ-oxalato-κO,O:O,O].

In the title complex, [Cd(C(2)O(4))(C(12)H(8)N(2))](n), the Cd(II) atom has a distorted octa-hedral coordination, defined by four O atoms from two symmetry-related oxalate ligands and by two N atoms from a bidentate 1,10-phenanthroline ligand. Each oxalate ligand bridges two Cd(II) atoms, generating a zigzag chain structure propagating along [100]. The packing of the structure is consolidated by non-classical C-H⋯O hydrogen-bonding inter-actions.