Search

(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]

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.

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]