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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]

Objective: For improving the efficiency of transferring medical records for stroke patients undergoing interhospital transfer, we evaluated what is the essential medical information for physicians using the Delphi method. Design: We set up an expert panel of 31 physiatrists, who participated in this study. The 32 preliminary items of the transferred medical information were listed by a physiatrist for the first round of the Delphi method, and degree of necessity for these 32 items was evaluated using a 3-point scale ("very necessary," "necessary," and "not necessary"). We considered "very necessary" and "necessary" as "agreed to its necessity." According to the Delphi method, a satisfactory level of consensus can be achieved with the agreement of significant majority (≥80%) in the expert panel. Results: Based on the experts' feedback, some items were added as the preliminary items. After the second round of the Delphi method, the items confirmed to be necessary information during interhospital transfer were motor and sensory evoked potentials, Barthel Index, Mini-Mental State Examination/Global Deterioration Scale, Motor-Free Visual Perception Test, Manual Function Test, Purdue Pegboard Test, hand grip power, monofilament, 2-point discrimination test, Manual Muscle Test, Nottingham Scale, modified Brunnstrom Classification, functional ambulation category, Glasgow Coma Scale, language function test, imaging study, videofluoroscopic swallowing study, rehabilitation goal, previous medical history, comorbidity, and medication information. Conclusions: If the previously mentioned necessary items are presented together at once during interhospital transfer, physicians who receive new stroke patients can evaluate patients' medical information more easily and completely. [ABSTRACT FROM AUTHOR]

Background: Transforaminal epidural steroid injections (TFESI) are widely used to alleviate lumbosacral radicular pain. Knowledge of the therapeutic outcomes of TFESI allows clinicians to elucidate therapeutic plans for managing lumbosacral radicular pain. Deep learning (DL) can outperform traditional machine learning techniques and learn from unstructured and perceptual data. A convolutional neural network (CNN) is a representative DL model., Objectives: We developed and investigated the accuracy of a CNN model for predicting therapeutic outcomes after TFESI for controlling chronic lumbosacral radicular pain using T2-weighted sagittal lumbar spine magnetic resonance (MR) images as input data., Study Design: Imaging study using DL., Setting: At the spine center of a university hospital., Methods: We collected whole T2-weighted sagittal lumbar spine MR images from 503 patients with chronic lumbosacral radicular pain due to a herniated lumbar disc (HLD) and spinal stenosis. A "good outcome" was defined as a >= 50% reduction in the numeric rating scale (NRS-11) score at 2 months after TFESI vs the pretreatment NRS-11 score. A "poor outcome" was defined as a < 50% decrease in the NRS-11 score at 2 months after TFESI vs pretreatment., Results: In the prediction of therapeutic outcomes after TFESI on the validation dataset, the area under the curve was 0.827., Limitations: Our study was limited in that we used a small amount of lumbar spine MR imaging data to train the CNN model., Conclusions: We demonstrated that a CNN model trained, using whole lumbar spine sagittal T2-weighted MR images, could help determine outcomes after TFESI in patients with chronic lumbosacral radicular pain due to an HLD or spinal stenosis.