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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]

Background: Sleep has been known to affect childhood development. Sleep disturbance is likely more common in children with developmental delay (DD) than in typical development. There are few studies on the correlation between sleep disturbance and developmental features in children with DD. Therefore, this study aimed to evaluate the associations between the two in children with DD. Methods: A total of 45 children (age range 27.0 ± 11.1) with DD were recruited and evaluated using the Sleep Disturbance Scale for Children (SDSC) and Bayley Scales of Infant and Toddler Development (BSID-III). The outcomes are expressed as means and standard deviations. The correlation between SDSC and BSID-III was assessed using Spearman's rank correlation test. Multiple regression analysis was performed to investigate the relationship between BSID-III domains and SDSC questionnaire subscales. Statistical significance was set at p < 0.05. Results: Based on the correlation analysis and subsequent hierarchical regression analysis, cognition and socio-emotional domains of BSID-III were significantly associated with the DOES subscale of the SDSC questionnaire. In addition, the expressive language domain of the BSID-III was found to be associated with the DA subscale of the SDSC questionnaire. It seems that excessive daytime sleepiness might negatively affect emotional and behavioral problems and cognitive function. Also, arousal disorders seem to be related to memory consolidation process, which is thought to affect language expression. Conclusion: This study demonstrated that DA and DOES subscales of the SDSC questionnaire were correlated with developmental aspects in preschool-aged children with DD. Sleep problems in children with DD can negatively affect their development, thereby interfering with the effectiveness of rehabilitation. Identifying and properly managing the modifiable factors of sleep problems is also crucial as a part of comprehensive rehabilitation treatment. Therefore, we should pay more attention to sleep problems, even in preschool-aged children with DD. [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]