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Human gait plays a crucial role in medical diagnostics, particularly in the context of adult spinal deformities (ASD). Recent research has applied deep-learning techniques to process video data for ASD diagnosis. Although this method successfully captured dynamic postural information, it does not account for the periodicity and symmetry of motion, particularly the periodicity of gait and the inherent symmetry of locomotor postures in the gait cycle. This omission may have led to the loss of critical information necessary for an accurate diagnosis. To resolve this issue, we present a method that can effectively capture cycle and action symmetry information in motion and exploit the characteristics of gait motion. The proposed method is subsequently trained using the fused data within the deep learning model. Our experiments, performed on a video dataset consisting of 81 patients, showed that our method outperformed baseline approaches. The proposed method achieved an accuracy of 71.43, precision of 72.80, and F1 score of 71.15. The complete set of codes, models, and results can be accessed from the GitHub repository: https://github.com/ChenKaiXuSan/Skeleton_ASD_PyTorch. In this study, we present an innovative video-based approach to support clinical diagnoses of gait disorders, with a focus on periodic motion and postural symmetry. The application of deep learning in diagnostic methods has been enhanced by utilizing the kinematic properties of walking movements. The experimental results demonstrate that the proposed method can provide more accurate diagnostic results when using limited video data.

Plant extracts have shown promise as natural feed additives to improve animal health and growth. Ellagic acid (EA), widely present in various plant tissues, offers diverse biological benefits. However, limited research has explored its effects on ruminants. This study aimed to investigate the effects of dietary addition EA on rumen metabolism, apparent digestibility of nutrients, and growth performance in Kazakh sheep. Ten 5-month-old Kazakh sheep with similar body weight (BW), fitted with rumen fistulas, were randomly assigned to two groups: the CON group (basal diet) and the EA group (basal diet + 30 mg/kg BW EA). The experiment lasted 30 days, and individual growth performance was assessed under identical feeding and management conditions. During the experimental period, rumen fluid, fecal, and blood samples were collected for analysis. The results indicated a trend toward increased average daily gain in the EA group compared to the CON group (p = 0.094). Compared with the CON group, the rumen contents of acetic acid and propionic acid were significantly increased in the EA group and reached the highest value at 2 h to 4 h after feeding (p < 0.05). Moreover, the relative abundances of specific rumen microbiota (Ruminococcaceae, uncultured_rumen_bacterium, unclassified_Prevotella, Bacteroidales, Bacteroidota, Bacteroidia, unclassified_Rikenellaceae, and Prevotella_spBP1_145) at the family and genus levels were significantly higher in the EA group (p < 0.05) compared to the CON group. The EA group exhibited significantly higher dry matter intake (p < 0.05) and increased the digestibility of neutral detergent fiber and ether extract when compared with the CON group (p < 0.05). Additionally, the plasma activities of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) were significantly higher, while malondialdehyde (MDA) concentration was significantly lower in the EA group compared to the CON group (p < 0.05). In conclusion, dietary supplementation with 30 mg/kg BW EA in 5-month-old Kazakh sheep increased the dry matter intakQ16e, apparent digestibility of neutral detergent fiber, and ether extract, as well as the contents of acetic acid and propionic acid in rumen fluid. Moreover, EA supplementation regulated the ruminal microbiota, enhanced antioxidant capacity, and improved daily weight gain.

IntroductionAssessment of human gait posture can be clinically effective in diagnosing human gait deformities early in life. Currently, two methods—static and dynamic—are used to diagnose adult spinal deformity (ASD) and other spinal disorders. Full-spine lateral standing radiographs are used in the standard static method. However, this is a static assessment of joints in the standing position and does not include information on joint changes when the patient walks. Careful observation of long-distance walking can provide a dynamic assessment that reveals an uncompensated posture; however, this increases the workload of medical practitioners. A three-dimensional (3D) motion system is proposed for the dynamic method. Although the motion system successfully detected dynamic posture changes, access to the facilities was limited. Therefore, a diagnostic approach that is facility-independent, has low practice flow, and does not involve patient contact is required.MethodsWe focused on a video-based method to classify patients with spinal disorders either as ASD, or other forms of ASD. To achieve this goal, we present a video-based two-stage machine-learning method. In the first stage, deep learning methods are used to locate the patient and extract the area where the patient is located. In the second stage, a 3D CNN (convolutional neural network) device is used to capture spatial and temporal information (dynamic motion) from the extracted frames. Disease classification is performed by discerning posture and gait from the extracted frames. Model performance was assessed using the mean accuracy, F1 score, and area under the receiver operating characteristic curve (AUROC), with five-fold cross-validation. We also compared the final results with professional observations.ResultsOur experiments were conducted using a gait video dataset comprising 81 patients. The experimental results indicated that our method is effective for classifying ASD and other spinal disorders. The proposed method achieved a mean accuracy of 0.7553, an F1 score of 0.7063, and an AUROC score of 0.7864. Additionally, ablation experiments indicated the importance of the first stage (detection stage) and transfer learning of our proposed method.DiscussionThe observations from the two doctors were compared using the proposed method. The mean accuracies observed by the two doctors were 0.4815 and 0.5247, with AUROC scores of 0.5185 and 0.5463, respectively. We proved that the proposed method can achieve accurate and reliable medical testing results compared with doctors' observations using videos of 1 s duration. All our code, models, and results are available at https://github.com/ChenKaiXuSan/Walk_Video_PyTorch. The proposed framework provides a potential video-based method for improving the clinical diagnosis for ASD and non-ASD. This framework might, in turn, benefit both patients and clinicians to treat the disease quickly and directly and further reduce facility dependency and data-driven systems.