Risk factors of video urodynamics and bladder management for long-term complications in patients with chronic spinal cord injury.

This study explores 15-year urological complications in chronic spinal cord injury (SCI) patients and investigates the predictive factors from video-urodynamic study (VUDS) and bladder management. Analyzing 864 SCI patients with a mean 15.6-year follow-up, we assessed complications and utilized multivariate logistic regression for risk evaluation. VUDS factors such as autonomic dysreflexia, detrusor sphincter dyssynergia, vesicourethral reflux (VUR), contracted bladder, and high voiding detrusor pressure significantly increased the likelihood of recurrent urinary tract infections (rUTI). Low bladder compliance, VUR, and contracted bladder notably raised the risk of hydronephrosis, while contracted bladder and detrusor overactivity with detrusor underactivity heightened chronic kidney disease risk. Volitional voiding reduced rUTI and VUR risk, whereas Valsalva maneuver-assisted voiding increased hydronephrosis risk. In conclusion, a contracted bladder identified in VUDS is associated with long-term urological complications in SCI, we propose that patients already experiencing a contracted bladder should prioritize volitional voiding as their preferred bladder management strategy to minimize the risk of additional complications such as rUTI and VUR. These findings unveil previously unexplored aspects in research, emphasizing the need for proactive management strategies in this patient population.

Exploring the impact of nonverbal social behavior on learning outcomes in instructional video design.

Online education has become increasingly popular in recent years, and video lectures have emerged as a common instructional format. While the importance of instructors' nonverbal social cues such as gaze, facial expression, and gestures for learning progress in face-to-face teaching is well-established, their impact on instructional videos is not fully understood. Most studies on nonverbal social cues in instructional videos focus on isolated cues rather than considering multimodal nonverbal behavior patterns and their effects on the learning progress. This study examines the role of instructors' nonverbal immediacy (a construct capturing multimodal nonverbal behaviors that reduce psychological distance) in video lectures with respect to learners' cognitive, affective, and motivational outcomes. We carried out an eye-tracking experiment with 87 participants (Mage = 24.11, SD = 4.80). Results of multilevel path analyses indicate that high nonverbal immediacy substantially increases learners' state motivation and enjoyment, but does not affect cognitive learning. Analyses of learners' eye movements show that learners allocate more attention to the instructor than to the learning material with increasing levels of nonverbal immediacy displayed by the instructor. The study highlights the importance of considering the role of multimodal nonverbal behavior patterns in online education and provides insights for effective video lecture design.

Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea.

BACKGROUND: The COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea. METHODS: The analysis was based on a cognitive-emotional model that links fear and anger to the social causes that trigger them and used discursive content from comments posted on YouTube's COVID-19-related videos. A total of 182,915 comments from 1,200 videos were collected between January and December 2020. We performed data analyses and visualizations using R, Netminer 4.0, and Gephi software and calculated Pearson's correlation coefficients between emotions. RESULTS: YouTube videos were analyzed for keywords indicating cognitive assessments of major events related to COVID-19 and keywords indicating negative emotions. Eight topics were identified through topic modeling: causes and risks, perceptions of China, media and information, infection prevention rules, economic activity, school and infection, political leaders, and religion, politics, and infection. The correlation coefficient between fear and anger was 0.462 (p < .001), indicating a moderate linear relationship between the two emotions. Fear was the highest from January to March in the first year of the COVID-19 outbreak, while anger occurred before and after the outbreak, with fluctuations in both emotions during this period. CONCLUSIONS: This study confirmed that social cognitions and negative emotions are intertwined in response to major events related to the COVID-19 pandemic, with each emotion varying individually rather than being ambiguously mixed. These findings could aid in developing social cognition-emotion-based public health strategies through education and communication during future pandemic outbreaks.

Evaluating the influence of a 3-min online video on the community knowledge of stroke in four Arab countries.

Introduction: Studies from developed and developing countries showed that the knowledge levels of stroke need improvement. Educational campaigns varied and were of limited influence predominantly because of their short duration and the need for financial support. The study aims to test the impact of a 3-min online video on the knowledge of stroke and factors influencing the knowledge score in four Arab countries. Methods: A cross-sectional web-based pre-post study was conducted in Egypt, Jordan, Lebanon, and the United Arab Emirates. The data were collected using the snowball technique. Participants were adults aged 18 years and above. The questionnaire sequence was conducting a pretest, followed by the educational video explaining stroke occurrence, types, risks, warning signs, preventive measures, and treatment, and finally, a posttest to evaluate the differences in knowledge from baseline. Statistical analysis included paired t-tests comparing pre-post-education stroke knowledge scores, while repeated measures ANOVA, adjusting for covariates, assessed mean changes. Results: The total number of participants was 2,721, mainly younger than 55 years. The majority had a university degree and were not healthcare professionals. A significant improvement was noted in the total knowledge score in all countries from a mean average (Mpretest = 21.11; Mposttest = 23.70) with p < 0.001. Identification of the stroke risks (Mpretest = 7.40; Mposttest = 8.75) and warning signs (Mpretest = 4.19; Mposttest = 4.94), understanding the preventive measures (Mpretest = 5.27; Mposttest = 5.39) and the importance of acting fast (Mpretest = 0.82; Mposttest = 0.85) improved from baseline with (p < 0.001) for all score components. Conclusion: The educational tool successfully enhanced public understanding of stroke risks, the identification of stroke signs, and the critical need for emergency action. The advantages of this video include its short length, free online access, use of evidence-based content in lay language, and reflective images. The ultimate goal remains the long-term improvement of sustainability by mandating full-scale trials.

Automated detection of steps in videos of strabismus surgery using deep learning.

BACKGROUND: Learning to perform strabismus surgery is an essential aspect of ophthalmologists' surgical training. Automated classification strategy for surgical steps can improve the effectiveness of training curricula and the efficient evaluation of residents' performance. To this end, we aimed to develop and validate a deep learning (DL) model for automated detecting strabismus surgery steps in the videos. METHODS: In this study, we gathered 479 strabismus surgery videos from Shanghai Children's Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, spanning July 2017 to October 2021. The videos were manually cut into 3345 clips of the eight strabismus surgical steps based on the International Council of Ophthalmology's Ophthalmology Surgical Competency Assessment Rubrics (ICO-OSCAR: strabismus). The videos dataset was randomly split by eye-level into a training (60%), validation (20%) and testing dataset (20%). We evaluated two hybrid DL algorithms: a Recurrent Neural Network (RNN) based and a Transformer-based model. The evaluation metrics included: accuracy, area under the receiver operating characteristic curve, precision, recall and F1-score. RESULTS: DL models identified the steps in video clips of strabismus surgery achieved macro-average AUC of 1.00 (95% CI 1.00-1.00) with Transformer-based model and 0.98 (95% CI 0.97-1.00) with RNN-based model, respectively. The Transformer-based model yielded a higher accuracy compared with RNN-based models (0.96 vs. 0.83, p < 0.001). In detecting different steps of strabismus surgery, the predictive ability of the Transformer-based model was better than that of the RNN. Precision ranged between 0.90 and 1 for the Transformer-based model and 0.75 to 0.94 for the RNN-based model. The f1-score ranged between 0.93 and 1 for the Transformer-based model and 0.78 to 0.92 for the RNN-based model. CONCLUSION: The DL models can automate identify video steps of strabismus surgery with high accuracy and Transformer-based algorithms show excellent performance when modeling spatiotemporal features of video frames.

Behavioral dishonesty in multiscenes: Associations with trait honesty and neural patterns during (dis)honesty video-watching.

Cross-situational inconsistency is common in the expression of honesty traits; yet, there is insufficient emphasis on behavioral dishonesty across multiple contexts. The current study aimed to investigate behavioral dishonesty in various contexts and reveal the associations between trait honesty, behavioral dishonesty, and neural patterns of observing others behave honestly or dishonestly in videos (abbr.: (dis)honesty video-watching). First, the results revealed limitations in using trait honesty to reflect variations in dishonest behaviors and predict behavioral dishonesty. The finding highlights the importance of considering neural patterns in understanding and predicting dishonest behaviors. Second, by comparing the predictive performance of seven types of data across three neural networks, the results showed that functional connectivity in the hypothesis-driven network during (dis)honesty video-watching provided the highest predictive power in predicting multitask behavioral dishonesty. Last, by applying the feature elimination method, the midline self-referential regions (medial prefrontal cortex, posterior cingulate cortex, and anterior cingulate cortex), anterior insula, and striatum were identified as the most informative brain regions in predicting behavioral dishonesty. In summary, the study offered insights into individual differences in deception and the intricate connections among trait honesty, behavioral dishonesty, and neural patterns during (dis)honesty video-watching.

How to edit a surgical case video.

A video can help highlight the real-time steps, anatomy and the technical aspects of a case that may be difficult to convey with text or static images alone. Editing with a regimented workflow allows for the transmission of only essential information to the viewer while maximizing efficiency by going through the editing process. This video tutorial breaks down the fundamentals of surgical video editing with tips and pointers to simplify the workflow.

Effect of video angle on detection of induced front limb lameness in horses.

BACKGROUND: Lameness examinations are commonly performed in equine medicine. Advancements in digital technology have increased the use of video recordings for lameness assessment, however, standardization of ideal video angle is not available yielding videos of poor diagnostic quality. The objective of this study was to evaluate the effect of video angle on the subjective assessment of front limb lameness. A randomized, blinded, crossover study was performed. Six horses with and without mechanically induced forelimb solar pain were recorded using 9 video angles including horses trotting directly away and towards the video camera, horses trotting away and towards a video camera placed to the left and right side of midline, and horses trotting in a circle with the video camera placed on the inside and outside of the circle. Videos were randomized and assessed by three expert equine veterinarians using a 0-5 point scoring system. Objective lameness parameters were collected using a body-mounted inertial sensor system (Lameness Locator®, Equinosis LLC). Interobserver agreement for subjective lameness scores and ease of grading scores were determined. RESULTS: Induction of lameness was successful in all horses. There was excellent agreement between objective lameness parameters and subjective lameness scores (AUC of the ROC = 0.87). For horses in the "lame" trials, interobserver agreement was moderate for video angle 2 when degree of lameness was considered and perfect for video angle 2 and 9 when lameness was considered as a binary outcome. All other angles had no to fair agreement. For horses in the "sound" trials, interobserver agreement was perfect for video angle 5. All other video angles had slight to moderate agreement. CONCLUSIONS: When video assessment of forelimb lameness is required, a video of the horse trotting directly towards the video camera at a minimum is recommended. Other video angles may provide supportive information regarding lameness characteristics.

Detecting the symptoms of Parkinson's disease with non-standard video.

BACKGROUND: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures. METHODS: Twenty-eight individuals with Parkinson's disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person's body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms. RESULTS: The support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively. CONCLUSION: These results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

Is social media our new quitline? A descriptive study assessing youtube coverage of tobacco cessation.

Background: Tobacco use and exposure are leading causes of morbidity and mortality worldwide. In the past decade, educational efforts to reduce tobacco use and exposure have extended to social media, including video-sharing platforms. YouTube is one of the most publicly accessed video-sharing platforms. Purpose: This cross-sectional descriptive study was conducted to identify and describe sources, formats, and content of widely viewed YouTube videos on smoking cessation. Methods: In August to September 2023, the keywords "stop quit smoking" were used to search in YouTube and identify 100 videos with the highest view count. Results: Collectively, these videos were viewed over 220 million times. The majority (n = 35) were posted by nongovernmental/organization sources, with a smaller number posted by consumers (n = 25), and only eleven were posted by governmental agencies. The format used in the highest number of videos was the testimonial (n = 32 videos, over 77 million views). Other popular formats included animation (n = 23 videos, over 90 million views) and talk by professional (n = 20 videos, almost 43 million views). Video content included evidence-based and non-evidence-based practices. Evidence-based strategies aligned with U.S. Public Health Service Tobacco Treatment Guidelines (e.g. health systems approach in tobacco treatment, medication management). Non-evidence-based strategies included mindfulness and hypnotherapy. One key finding was that environmental tobacco exposure received scant coverage across the videos. Conclusions: Social media such as YouTube promises to reach large audiences at low cost without requiring high reading literacy. Additional attention is needed to create videos with up-to-date, accurate information that can engage consumers.

Cost-effort analysis of Baited Remote Underwater Video (BRUV) and environmental DNA (eDNA) in monitoring marine ecological communities.

Monitoring the diversity and distribution of species in an ecosystem is essential to assess the success of restoration strategies. Implementing biomonitoring methods, which provide a comprehensive assessment of species diversity and mitigate biases in data collection, holds significant importance in biodiversity research. Additionally, ensuring that these methods are cost-efficient and require minimal effort is crucial for effective environmental monitoring. In this study we compare the efficiency of species detection, the cost and the effort of two non-destructive sampling techniques: Baited Remote Underwater Video (BRUV) and environmental DNA (eDNA) metabarcoding to survey marine vertebrate species. Comparisons were conducted along the Sussex coast upon the introduction of the Nearshore Trawling Byelaw. This Byelaw aims to boost the recovery of the dense kelp beds and the associated biodiversity that existed in the 1980s. We show that overall BRUV surveys are more affordable than eDNA, however, eDNA detects almost three times as many species as BRUV. eDNA and BRUV surveys are comparable in terms of effort required for each method, unless eDNA analysis is carried out externally, in which case eDNA requires less effort for the lead researchers. Furthermore, we show that increased eDNA replication yields more informative results on community structure. We found that using both methods in conjunction provides a more complete view of biodiversity, with BRUV data supplementing eDNA monitoring by recording species missed by eDNA and by providing additional environmental and life history metrics. The results from this study will serve as a baseline of the marine vertebrate community in Sussex Bay allowing future biodiversity monitoring research projects to understand community structure as the ecosystem recovers following the removal of trawling fishing pressure. Although this study was regional, the findings presented herein have relevance to marine biodiversity and conservation monitoring programs around the globe.

Clinical value of video oculomotor evaluation in the differential diagnosis of multiple system atrophy and Parkinson's disease.

BACKGROUND: Multiple system atrophy (MSA) is a neurodegenerative disease that progresses rapidly and has a poor prognosis. This study aimed to assess the value of video oculomotor evaluation (VOE) in the differential diagnosis of MSA and Parkinson's disease (PD). METHODS: In total, 28 patients with MSA, 31 patients with PD, and 30 age- and sex-matched healthy controls (HC) were screened and included in this study. The evaluation consisted of a gaze-holding test, smooth pursuit eye movement (SPEM), random saccade, and optokinetic nystagmus (OKN). RESULTS: The MSA and PD groups had more abnormalities and decreased SPEM gain than the HC group (64.29%, 35.48%, 10%, p < .001). The SPEM gain in the MSA group was significantly lower than that in the PD group at specific frequencies. Patients with MSA and PD showed prolonged latencies in all saccade directions compared with those with HC. However, the two diseases had no significant differences in the saccade parameters. The OKN gain gradually decreased from the HC to the PD and the MSA groups (p < .05). Compared with the PD group, the gain in the MSA group was further decreased in the OKN test at 30°/s (Left, p = .010; Right p = .016). Receiver operating characteristic curves showed that the combination of oculomotor parameters with age and course of disease could aid in the differential diagnosis of patients with MSA and PD, with a sensitivity of 89.29% and a specificity of 70.97%. CONCLUSIONS: The combination of oculomotor parameters and clinical data may aid in the differential diagnosis of MSA and PD. Furthermore, VOE is vital in the identification of neurodegenerative diseases.

Markerless 3D kinematics and force estimation in cheetahs.

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (Acinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal's motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94% PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3N ( ≈ 17.16% of peak force during stride) for the estimated ground reaction force when compared against the force plate data. While the joint torques cannot be directly validated against ground truth data, as no such data is available for cheetahs, the estimated torques agree with previous studies of quadrupeds in controlled settings. These results will enable deeper insight into the study of animal locomotion in a more natural environment for both biologists and roboticists.

Adaptive temporal compression for reduction of computational complexity in human behavior recognition.

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Biomechanics of biting in loggerhead shrikes: jaw-closing force, velocity and an argument for power.

Differences in the physical and behavioral attributes of prey are likely to impose disparate demands of force and speed on the jaws of a predator. Because of biomechanical trade-offs between force and speed, this presents an interesting conundrum for predators of diverse prey types. Loggerhead shrikes (Lanius ludovicianus) are medium-sized (∼50 g) passeriform birds that dispatch and feed on a variety of arthropod and vertebrate prey, primarily using their beaks. We used high-speed video of shrikes biting a force transducer in lateral view to obtain corresponding measurements of bite force, upper and lower bill linear and angular displacements, and velocities. Our results show that upper bill depression (about the craniofacial hinge) is more highly correlated with bite force, whereas lower bill elevation is more highly correlated with jaw-closing velocity. These results suggest that the upper and lower jaws might play different roles for generating force and speed (respectively) in these and perhaps other birds as well. We hypothesize that a division of labor between the jaws may allow shrikes to capitalize on elements of force and speed without compromising performance. As expected on theoretical grounds, bite force trades-off against jaw-closing velocity during the act of biting, although peak bite force and jaw-closing velocity across individual shrikes show no clear signs of a force-velocity trade-off. As a result, shrikes appear to bite with jaw-closing velocities and forces that maximize biting power, which may be selectively advantageous for predators of diverse prey that require both jaw-closing force and speed.

Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities.

Utilization of hand-tracking cameras, such as Leap, for hand rehabilitation and functional assessments is an innovative approach to providing affordable alternatives for people with disabilities. However, prior to deploying these commercially-available tools, a thorough evaluation of their performance for disabled populations is necessary. In this study, we provide an in-depth analysis of the accuracy of Leap's hand-tracking feature for both individuals with and without upper-body disabilities for common dynamic tasks used in rehabilitation. Leap is compared against motion capture with conventional techniques such as signal correlations, mean absolute errors, and digit segment length estimation. We also propose the use of dimensionality reduction techniques, such as Principal Component Analysis (PCA), to capture the complex, high-dimensional signal spaces of the hand. We found that Leap's hand-tracking performance did not differ between individuals with and without disabilities, yielding average signal correlations between 0.7-0.9. Both low and high mean absolute errors (between 10-80mm) were observed across participants. Overall, Leap did well with general hand posture tracking, with the largest errors associated with the tracking of the index finger. Leap's hand model was found to be most inaccurate in the proximal digit segment, underestimating digit lengths with errors as high as 18mm. Using PCA to quantify differences between the high-dimensional spaces of Leap and motion capture showed that high correlations between latent space projections were associated with high accuracy in the original signal space. These results point to the potential of low-dimensional representations of complex hand movements to support hand rehabilitation and assessment.

Comparative analysis of GoPro and digital cameras in head and neck flap harvesting surgery video documentation: an innovative and efficient method for surgical education.

BACKGROUND: An urgent need exists for innovative surgical video recording techniques in head and neck reconstructive surgeries, particularly in low- and middle-income countries where a surge in surgical procedures necessitates more skilled surgeons. This demand, significantly intensified by the COVID-19 pandemic, highlights the critical role of surgical videos in medical education. We aimed to identify a straightforward, high-quality approach to recording surgical videos at a low economic cost in the operating room, thereby contributing to enhanced patient care. METHODS: The recording was comprised of six head and neck flap harvesting surgeries using GoPro or two types of digital cameras. Data were extracted from the recorded videos and their subsequent editing process. Some of the participants were subsequently interviewed. RESULTS: Both cameras, set at 4 K resolution and 30 frames per second (fps), produced satisfactory results. The GoPro, worn on the surgeon's head, moves in sync with the surgeon, offering a unique first-person perspective of the operation without needing an additional assistant. Though cost-effective and efficient, it lacks a zoom feature essential for close-up views. In contrast, while requiring occasional repositioning, the digital camera captures finer anatomical details due to its superior image quality and zoom capabilities. CONCLUSION: Merging these two systems could significantly advance the field of surgical video recording. This innovation holds promise for enhancing technical communication and bolstering video-based medical education, potentially addressing the global shortage of specialized surgeons.