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(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model's robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years. [ABSTRACT FROM AUTHOR]

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction. [ABSTRACT FROM AUTHOR]

Therapeutic management of pain in patients with complex regional pain syndrome (CRPS) is challenging. Repetitive transcranial magnetic stimulation (rTMS) has analgesic effects on several types of pain. However, its effect on CRPS has not been elucidated clearly. Therefore, we conducted a meta-analysis of the available clinical studies on rTMS treatment in patients with CRPS.

Giga-casting, a revolutionary approach for manufacturing large, single-piece car body components from aluminium, has emerged as a potential game-changer in the automotive industry. However, these large, thin-walled castings are prone to distortions during solidification and heat treatment processes. Straightening these distortions is crucial to ensure structural integrity, facilitate downstream assembly, and maintain aesthetic qualities. This paper proposes a novel method for straightening giga-cast components using a multi-pin straightening machine. The machine's versatility stems from its ability to adapt to various geometries through multiple strategically controlled straightening pins. This paper introduces the concept of a "straightening stroke decision algorithm" to achieve precise straightening and overcome the challenges of complex shapes. This algorithm determines the stroke length for each pin, combining a polynomial model representing the global stiffness of the component with a machine learning model that captures the stiffness changes arising from the current geometry. The effectiveness of the proposed approach is evaluated through comprehensive numerical experiments using finite element analyses. The straightening performance is assessed for the straightening algorithm with different machine learning models (deep neural network and XGBoost) and compared to a traditional optimisation method. The proposed surrogate models decided the straightening strokes so that the maximum remaining distortion became 0.02% of the largest dimension of each target geometry. The results of the numerical experiment showed that the proposed straightening method is suitable for straightening distortion in large thin-walled components. [ABSTRACT FROM AUTHOR]

BACKGROUND: Despite the application of various therapeutic methods, pain caused by complex regional pain syndrome (CRPS) is not sufficiently managed and often progresses to a chronic stage. For the systematic and effective treatment of CRPS, we developed an algorithm for multimodal medication therapy based on the established pathophysiology of CRPS to control CRPS-related pain. OBJECTIVE: In this study, we present the outcomes of our novel algorithm for multimodal medication therapy for patients with CRPS, consisting of three major components: multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapy. METHODS: We retrospectively investigated patients with CRPS who received multimodal therapy. Pain severity scores were evaluated using a numerical rating scale at four time points (P1, pain at initial consultation; P2, pain after oral medication; P3, pain after ketamine treatment; and P4, pain after lidocaine treatment). The effect of the multimodal medication therapy algorithm on pain management was evaluated at each time point. RESULTS: In patients with CRPS, multimodal oral medication, intravenous ketamine, and intravenous lidocaine therapies led to significantly improved pain control (p < 0.05). Additionally, the combination of these three therapies (through the multimodal medication therapy algorithm) resulted in significant pain relief in patients with CRPS (p < 0.05). CONCLUSIONS: Our multimodal medication therapy algorithm effectively controlled pain in patients with CRPS. However, further prospective studies with large sample sizes and randomized controlled trials are needed for more accurate generalization. [ABSTRACT FROM AUTHOR]