Background: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of singlelead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored.This study introduces a method to identify AF using single-lead mobile ECG during NSR. Methods: We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy. Results: ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68. Conclusion The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

OBJECTIVE: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MATERIALS AND METHODS: MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. RESULTS: Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. CONCLUSION: The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.
Archives ; Consensus ; Glioblastoma* ; Humans ; Male

Objective: To identify the relationship between dysphagia, dietary level, and pulmonary function in post-stroke patients. Methods: Thirty-six post-stroke patients with dysphagia, who were hospitalized from June 2017 to October 2017 in the Department of Rehabilitation Medicine at a tertiary hospital, were analyzed retrospectively. The video-fluoroscopic swallowing study (VFSS) and videofluoroscopic dysphagia scale (VDS) were used to assess dysphagia. The vital capacity (VC) and peak cough flow (PCF) were used to assess the pulmonary function. Upon admission, the patients were divided into three groups according to their dietary level (tube feeding, dysphagia diet, and general diet). The correlation between dysphagia and pulmonary function was analyzed using an independent t-test test with the optimal points, and the relationship between the diet level and pulmonary function was evaluated using a one-way analysis of the variance. Results: Significant correlations between the pulmonary function and sub-items of VDS were found in “oral transit time” with VC, “vallecullar residue” and “aspiration” with PCF, and “triggering of pharyngeal swallow”, “VDS total score” with VC and PCF. The dietary levels upon admission had a significant correlation with VC and PCF. The VC among groups divided according to three diet levels showed statistically significant differences. Conclusion This study revealed the relationship between the pulmonary function and dysphagia in post-stroke patients. Moreover, the pulmonary function correlated with dietary level, even though it was not confirmed that it affected dietary levels. The clinical importance of the pulmonary function in post-stroke patients with dysphagia should be emphasized. In addition, a large-scale study is needed to determine the correlation between the pulmonary function and swallowing difficulty