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Skeletal muscle index measurement via dual-energy X-ray absorptiometry or bioelectrical impedance analysis is used to evaluate muscle mass when diagnosing sarcopenia. However, inherent challenges exist with these methods. We previously focused on muscle mass evaluation in the quadriceps femoris by using computed tomography (CT). In this observational study, we utilized a new ultrasound device with a wide field of view that can obtain CT-like images and investigated its usefulness. Reproducibility was investigated by calculating the intra- and inter-examiner intraclass correlation coefficient (ICC) by using three examiners and performing five measurements in 12 participants. In 123 participants (48 men, 75 women, mean age 78.2 ± 8.1 years), we investigated the correlation between the quadriceps cross-sectional area measured with supine ultrasound and CT images as well as between supine and sitting ultrasound images. Unadjusted, age-adjusted, and age–sex-adjusted correlations were calculated. Reproducibility was excellent (intra-examiner ICC[1,1]: 0.978, 0.987, and 0.994; inter-examiner ICC[2,1]: 0.993). The unadjusted, age-adjusted, and age–sex-adjusted correlations between the quadriceps cross-sectional area measured using supine ultrasound and CT were 0.949, 0.940, and 0.894, respectively. For sitting ultrasound, the corresponding values were 0.958, 0.953, and 0.912, respectively. Correlations between the supine and sitting ultrasound measurements were also good, with corresponding values of 0.952, 0.945, and 0.904, respectively. The tested ultrasound device showed excellent measurement reproducibility and had good correlations with CT images. Further studies with an increased numbers of clinical cases and additional evaluations should allow the device to become a screening tool for diagnosing sarcopenia. [ABSTRACT FROM AUTHOR]

Aim A testing method for early diagnosis of Mild cognitive dementia (MCI) that can be easily applied in clinical practice was investigated in this study. We examined whether MCI risk can be determined through finger movements. Methods Between 2013 and 2020, 1097 individuals were screened. After applying propensity-score matching to adjust for variability between the groups, 173 individuals each in the mild cognitive impairment and control groups were selected. Thereafter, differences between groups in mean values of parameters extracted from finger tap movements were determined using unpaired t-test and effect size. Furthermore, area under the curve, sensitivity, and specificity were calculated from the receiver operating characteristic curve for parameters with significant difference. Results A significant difference was observed, especially in the number of taps in the MCI group compared with that in the control group ( p < .001; 95% CI, −12.7 to −8.8; r = 0.51). A cut-off value of 30 taps was applied (sensitivity, 0.77; specificity, 0.67; AUC, 0.79). Significant differences were also observed in rhythm-related parameters. Conclusions These parameters might be useful for capturing MCI risk. Finger taps are easily measured and may be suitable for screening large populations. This tool might be used as a supplemental method to increase the sensitivity of traditional cognitive tests.

Traffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: The association of the combination of body mass index (BMI) and weight change at admission with prognoses in patients with heart failure (HF) is unclear. Therefore, we investigated whether BMI and weight changes at admission affect mortality in patients with HF. Methods: This retrospective cohort study lasted 99 months, starting in April 2014, and included 4,862 patients with HF from a Japanese real-world database. Cubic and thin-plate smoothing spline analyses were performed to investigate the association of BMI and weight changes with mortality. The percentage weight change was calculated every 6 months. The study outcome was the presence or absence of death. Results: The patients' mean age was 81.5±9.6 years, and 1,239 (25.5%) patients died. Cubic spline analysis revealed a negative correlation of BMI with mortality hazard ratio (HR) (BMI of 18.5 kg/m2 and 25 kg/m2; HR=1.3 [1.2-1.4] and 0.8 [0.7-0.9], respectively). Cubic spline analysis of weight change showed that weight loss tended to increase the mortality HR (each 6% decrease in weight change rate was associated with a 1.1 times higher mortality risk (95% CI [1.0-1.2]). Thin-plate smoothing spline analysis showed that the odds ratio (OR) negatively correlated with BMI (1-year mortality: BMI of 18.5 kg/m², 22 kg/m², and 25 kg/m²; OR at 0% weight change=1.5, 1.0, and 0.7, respectively; 2-year mortality: BMI=18.5 kg/m², 22 kg/ m², and 25 kg/m²; OR at 0% weight change=1.4, 0.9, and 0.7, respectively). Conclusion: A low BMI in patients with HF was associated with a higher risk of mortality. Weight loss in patients, regardless of BMI, was associated with a higher OR for mortality. [ABSTRACT FROM AUTHOR]

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players' importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images. [ABSTRACT FROM AUTHOR]

This study proposes an automatic findings generation method that performs in-context few-shot learning of a visual language model. The automatic generation of findings can reduce the burden of creating inspection records for infrastructure facilities. However, the findings must include the opinions and judgments of engineers, in addition to what is recognized from the image; therefore, the direct generation of findings is still challenging. With this background, we introduce in-context few-short learning that focuses on image similarity and text diversity in the visual language model, which enables text output with a highly accurate understanding of both vision and language. Based on a novel in-context few-shot learning strategy, the proposed method comprehensively considers the characteristics of the distress image and diverse findings and can achieve high accuracy in generating findings. In the experiments, the proposed method outperformed the comparative methods in generating findings for distress images captured during bridge inspections. [ABSTRACT FROM AUTHOR]

Psychological aspects of rehabilitation nutrition affect physical, cognitive, and social rehabilitation nutrition. When depression is recognized, not only pharmacotherapy and psychotherapy, but also non‐pharmacological therapies such as exercise, nutrition, psychosocial, and other interventions can be expected to improve depression. Therefore, accurate diagnosis and intervention without overlooking depression is important. Psychological aspects of preventive rehabilitation nutrition is also important because depression can be partially prevented by appropriate exercise and nutritional management. Even in the absence of psychological negatives, increasing more psychological positives from a positive psychology perspective can be useful for both patients and healthcare professionals. Positive rehabilitation nutrition interventions can increase more psychological positives, such as well‐being, through cognitive‐behavioral therapy and mindfulness on their own, as well as through interventions on environmental factors. Consequently, physical, cognitive, and social positives are also expected to be enhanced. [ABSTRACT FROM AUTHOR]