Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object detection models.

In demersal trawl fisheries, the unavailability of the catch information until the end of the catching process is a drawback, leading to seabed impacts, bycatches and reducing the economic performance of the fisheries. The emergence of in-trawl cameras to observe catches in real-time can provide such information. This data needs to be processed in real-time to determine the catch compositions and rates, eventually improving sustainability and economic performance of the fisheries. In this study, a real-time underwater video processing system counting the Nephrops individuals entering the trawl has been developed using object detection and tracking methods on an edge device (NVIDIA Jetson AGX Orin). Seven state-of-the-art YOLO models were tested to discover the appropriate training settings and YOLO model. To achieve real-time processing and accurate counting simultaneously, four frame skipping ideas were evaluated. It has been shown that adaptive frame skipping approach, together with YOLOv8s model, can increase the processing speed up to 97.47 FPS while achieving correct count rate and F-score of 82.57% and 0.86, respectively. In conclusion, this system can improve the sustainability of the Nephrops directed trawl fishery by providing catch information in real-time.

Measuring quality and safety of epilepsy monitoring units in Brazil: Adoption of quality indicators.

PURPOSE: Drug-resistant epilepsy affects a substantial proportion (30-40 %) of patients with epilepsy, often necessitating video-electroencephalography (video-EEG) monitoring. In 2016, Sauro et al. introduced a set of measures aimed at improving the quality and safety indicators reported in video-EEG evaluations. This study aims to report our experience with the implementation of these measures. METHODS: We analyzed video-EEG data regarding quality and safty from a period spanning January 2016 to January 2018, involving a total of 101 patients monitored in our video-EEG unit. RESULTS: Among the patients included in the study, a definitive diagnosis was attainable for 92.1 %, with 36.6 % experiencing a change in diagnosis and 65.3 % undergoing a change in treatment as a result of the video-EEG evaluation. Additionally, the referral question was fully addressed in 60.4 % of admissions, and video-EEG was considered to be very useful or extremely useful in 66.4 % of cases. Adverse events were observed in 26.7 % of patients, with the most common being the progression of focal seizures to bilateral tonic-clonic seizures (11.9 %) and the occurrence of seizure clusters (5.9 %). CONCLUSION: Our findings support the implementation of Sauro et al.'s set of measures, as they provide valuable criteria for improving the reporting of video-EEG quality and safety indicators. However, challenges may arise due to variations in terminology across studies and the lack of standardized criteria for defining essential questions in video-EEG evaluations. Further research utilizing these measures is necessary to enhance their effectiveness and encourage consistent reporting of results from epilepsy monitoring units.

Quality of YouTube® videos on thrombolysis and thrombectomy for deep venous thrombosis.

OBJECTIVE: YouTube® has gained popularity as an unofficial educational resource for surgical trainees, but its content's quality and educational value remain to be evaluated. The aim of this study is to analyze the current content on these techniques for lower extremity DVT (LEDVT) on YouTube®. METHODS: A search was performed on YouTube® using 13 search terms in August 2022 on a clear-cached browser. Open-access videos focusing on the surgical techniques of venous thrombolysis or thrombectomy for LEDVT were included. Quality and educational value were assessed and graded based on metrics for accountability (4 items), content (13 items), and production (9 items). RESULTS: Out of 138 videos regarding LEDVT oriented towards medical professionals, only 14 met inclusion criteria. Videos ran for a median of 3.4 min (range 0.37-35.6 min) with a median of 941 views (range 106-54624). Videos scored a median of 5.5 (range 1.0-8.0) out of 11 for content, a median of 2.0 out of 6.0 (range 0.0-2.0) for accountability, and a median of 5.5 out of 9.0 (range 3.0-9.0) for production. CONCLUSION: Few YouTube® videos focus on the technical aspects of DVT thrombolysis/thrombectomy, and they vary significantly in content with overall poor accountability and production quality.

Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

Objective: To overcome limitations of open surgery artificial intelligence (AI) models by curating the largest collection of annotated videos and to leverage this AI-ready data set to develop a generalizable multitask AI model capable of real-time understanding of clinically significant surgical behaviors in prospectively collected real-world surgical videos. Design, Setting, and Participants: The study team programmatically queried open surgery procedures on YouTube and manually annotated selected videos to create the AI-ready data set used to train a multitask AI model for 2 proof-of-concept studies, one generating surgical signatures that define the patterns of a given procedure and the other identifying kinematics of hand motion that correlate with surgeon skill level and experience. The Annotated Videos of Open Surgery (AVOS) data set includes 1997 videos from 23 open-surgical procedure types uploaded to YouTube from 50 countries over the last 15 years. Prospectively recorded surgical videos were collected from a single tertiary care academic medical center. Deidentified videos were recorded of surgeons performing open surgical procedures and analyzed for correlation with surgical training. Exposures: The multitask AI model was trained on the AI-ready video data set and then retrospectively applied to the prospectively collected video data set. Main Outcomes and Measures: Analysis of open surgical videos in near real-time, performance on AI-ready and prospectively collected videos, and quantification of surgeon skill. Results: Using the AI-ready data set, the study team developed a multitask AI model capable of real-time understanding of surgical behaviors-the building blocks of procedural flow and surgeon skill-across space and time. Through principal component analysis, a single compound skill feature was identified, composed of a linear combination of kinematic hand attributes. This feature was a significant discriminator between experienced surgeons and surgical trainees across 101 prospectively collected surgical videos of 14 operators. For each unit increase in the compound feature value, the odds of the operator being an experienced surgeon were 3.6 times higher (95% CI, 1.67-7.62; P = .001). Conclusions and Relevance: In this observational study, the AVOS-trained model was applied to analyze prospectively collected open surgical videos and identify kinematic descriptors of surgical skill related to efficiency of hand motion. The ability to provide AI-deduced insights into surgical structure and skill is valuable in optimizing surgical skill acquisition and ultimately improving surgical care.

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection.

Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection. Our model proposes a bimodal approach based on the concatenation of two distinct Convolutional Neural Networks processing two feature sets extracted from the same videos. The features obtained from the convolutions of both networks are subsequently flattened and merged into a single vector, serving as input for the fully connected layers in the binary classification process. This approach demonstrates the potential for effective ASD detection in children through the combination of gait analysis and deep learning techniques.

Current Quality of Videos on Colorectal Cancer Screening for General Public.

BACKGROUND: One-third of American adults encompassed by current colorectal cancer screening guidelines fail to obtain recommended screening evaluations. Educational videos are a valuable medium through which to educate and encourage recommended health behaviors in patients. METHODS: A cross-sectional study reviewing the quality of patient education videos addressing colorectal cancer screening. Video quality was assessed in 3 domains: accountability, content, and production. RESULTS: Forty-four videos met inclusion criteria. Out of 33 possible points, videos scored a median of 15.0 (interquartile range 12.9-16.6). Videos scored 1.0 (interquartile range .8-1.0) out of 4.0 for accountability, 6.0 (interquartile range 4.4-8.0) out of 20 for content, and 8.0 (interquartile range 7.4-8.0) out of 9.0 for production. Colonoscopy was the most frequently discussed method of screening (38, 86%). While 13 (34%) videos discussed the risk of colorectal cancer in the general population and 15 (32%) discussed the risk in those with a family history, few videos addressed those with other risk factors. Most (31, 70%) videos discussed the medical consequences of not receiving screening, but only 1 (2%) video discussed the social consequences. Similarly, medical benefits were discussed in 34 (77%) videos while other benefits were not discussed by any video. Only one-fifth of the videos address three or more barriers to screening. CONCLUSIONS: Videos on colorectal cancer screening have excellent production quality but need improvement in the domains of accountability and content. The videos included in this analysis did not adequately address the concerns of viewers nor the benefits of colorectal cancer screening.

Scalable Face Image Coding via StyleGAN Prior: Toward Compression for Human-Machine Collaborative Vision.

The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm's feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates (< 0.01 bpp), offering new insights for human-machine collaborative compression.

Using a Bougie With C-MAC Video Laryngoscopy Did Not Improve First-Attempt Intubation Success Rates in Critical Care Air Transport.

OBJECTIVE: Studies have shown a bougie improves first-attempt success rates when used in combination with direct laryngoscopy during the initial attempt. The purpose of this study was to determine whether the use of a bougie in combination with C-MAC (Karl Storz, Tuttlingen, Germany) improves first-attempt success rates of endotracheal intubation (ETI) compared with C-MAC with a traditional stylet. METHODS: This study is a retrospective chart review using data collected on 371 intubations completed by a single air medical service using the C-MAC laryngoscope and either a bougie or a stylet. RESULTS: The overall success rate using C-MAC for ETI with either a bougie or a stylet was 83%. There was no statistically significant difference between first-attempt successful intubations using C-MAC and a bougie (82%) or a stylet (86%) (χ1 = 0.871, P = .351). There was no statistically significant difference between laryngoscopy grade and the number of attempts that resulted in a successful intubation (χ1 = 0.743, P = .7). CONCLUSION: There was no difference between first-attempt success rates using video laryngoscopy with a bougie, overall intubation success rates, or difficult intubation success rates compared with video laryngoscopy with a stylet, indicating that the purpose of a bougie as a rescue device did not hold true in the prehospital setting of our critical care air medical service.

Comparative study of video recordings of non-medical devices in laparoscopic surgery: a cross-sectional study.

INTRODUCTION: Laparoscopic surgery is the approach of choice for multiple procedures, being laparoscopic cholecystectomy one of the most frequently performed surgeries. Likewise, video recording of these surgeries has become widespread. Currently, the market offers medical recording devices (MRD) with an approximate cost of 2000 USD, and alternative non-medical recording devices (NMRD) with a cost ranging from 120 to 200 USD. To our knowledge, no comparative studies between the available recording devices have been done. We aim to compare the perception of the quality of videos recorded by MRD and NMRD in a group of surgeons and surgical residents. METHODS: A cross-sectional study was conducted using an online survey to compare recordings from three NMRDs (Elgato 30 fps, AverMedia 60 fps, Hauppauge 30 fps) and one MRD (MediCap 20 fps) during a laparoscopic cholecystectomy. The survey assessed: definition of anatomical structures (DA), fluidity of movements (FM), similarity with the operating room screen (ORsim), and overall quality (OQ). Descriptive and nonparametric analytical statistics tests were applied. Results were analyzed using JMP-15 software. RESULTS: Forty surveys were collected (80% surgeons, 20% residents). NMRDs scored significantly higher than MRD in DA (p = 0.003), FM (p < 0.001), ORsim (p < 0.001), and OQ (p < 0.001). One NMRD was chosen as the highest quality device (70%), and MRD as the poorest (78%). No significant differences were found when analyzing by surgical experience. CONCLUSIONS: In terms of recording laparoscopic procedures, non-medical video recording devices (NMRDs) outperformed medical-grade recording device (MRD) with a higher overall score. This suggests that NMRDs could serve as a cost-effective alternative with superior video quality for recording laparoscopic surgeries.

YouTube and pudendal neuralgia: Is it a good source of information for patients?

OBJECTIVE: This study aims to identify the shortcomings and quality content of YouTube videos and its effectiveness as a source of patient information on pudendal neuralgia treatment. METHODS: A search was conducted on YouTube using the words "pudendal neuralgia physical therapy," "medications for pudendal neuralgia," "pudendal nerve block," "pudendal neuralgia surgery," and "alternative treatments for pudendal neuralgia." The results were analyzed based on the source, general descriptive statistics, the intended audience, and five content areas. The DISCERN scoring system was used to evaluate the quality of videos. RESULTS: After the search, 73 videos met the inclusion criteria for further analysis. The majority of these videos (61.64%) were intended to target the general population, whereas a smaller percentage were identified as professional (41.10%) or targeted for physicians (35.62%). From the videos included, 10 (13.70%) described treatment options in a balanced and evidence-based manner. The higher DISCERN score positively correlated with the presence of this last content criterion. With a total DISCERN mean score of 35.42, a significant proportion of the videos (41.10%) were rated very poor. The remaining videos were classified as poor (23.29%), fair (19.18%), good (8.22%), and excellent (8.22%). CONCLUSION: The quality of the information included in YouTube videos regarding pudendal neuralgia treatment was considered generally poor. Healthcare providers must recognize the potential influence of this platform on patients' understanding of pudendal neuralgia treatment. There is a need for additional research and randomized studies regarding YouTube content about this condition.

Captive Animal Behavior Study by Video Analysis.

Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat's head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis.

Analysis of YouTube content on substance use disorder treatment and recovery.

BACKGROUND AND AIM: Emerging literature suggests the role of social media in substance use disorders (SUD). This study aimed to explore the content of YouTube videos for persons on SUD treatment/recovery, describing the users' exposure and engagement metrics and understanding viewers' perspectives. METHODS: We generated a set of 10 key phrases to search on YouTube. Eighty eligible videos were analyzed using a mixed-methods approach. Content analysis of all videos and thematic analysis of 30 videos were done using the three most viewed videos from each key phrase. The reliability of videos was assessed using a modified DISCERN. The total number of views, likes, dislikes, and comments were noted and created engagement metrics. The linguistic analysis of viewers' comments was done to assess their perspectives. RESULTS: Sixty-three (78.8%) videos were from the US, and 59 (73.8%) were intended for persons or families with substance misuse. Persons in recovery uploaded 23 (28.7%) videos. We identified five themes - reasons for using drugs, symptoms of addiction, consequences of drug use, how to stop drug use, and expressed tone in the language. The positivity and relative positivity ratios were highest for videos developed by persons in recovery. There was a negative correlation between the relative positivity ratio and content fostering internalized stigma. Words with negative emotional experiences dominated the viewers' comments. CONCLUSION: YouTube content on SUD treatment and recovery is popular and revolves around the biopsychosocial understanding of addiction. There is an urgent need for a language policy and regulation of non-scientific content.

Comparison of automated video tracking systems in the open field test: ANY-Maze versus EthoVision XT.

BACKGROUND: ANY-Maze and EthoVision XT are two commonly used automated animal tracking systems employed to produce reliable and consistent results in behavioural paradigms. Data obtained with both tracking systems have presented differences, particularly when varying laboratory lighting conditions and contrasts of mice coat colour against the arena background in both water maze and tunnel maze. METHOD: In this study, two fluorescent lighting conditions (58 and 295 lux), local to our laboratory, and different coat-coloured mouse lines (C57BL/6 J - black; CD1 - agouti; C3H/HeN - white) were used to compare reproducibility in measures of tracking systems (ANY-Maze versus EthoVision) in the open field test. RESULTS: Differences between systems were reliant on the contrasts between coat and background colours. Surprisingly, black animals presented the greatest differences in read-outs between tracking systems, regardless of lighting conditions. Data from both video observation tools differed mainly in exploration-related parameters (distance travelled), but less in more static proxies (time in thigmotaxis zone). Overall, EthoVision XT returned higher values for most parameters analysed relative to ANY-Maze. More inconsistencies in recording and analysis can be expected from other video recording systems. CONCLUSION: Data analysis software provides an additional source of variation in need of consideration when reproducibility in behavioural neuroscience is required.

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings.

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging.

The development of a robust 3D imaging system for underwater applications is a crucial process in underwater imaging where the physical properties of the underwater environment make the implementation of such systems challenging. Calibration is an essential step in the application of such imaging systems and is performed to acquire the parameters of the image formation model and to enable 3D reconstruction. We present a novel calibration method for an underwater 3D imaging system comprising a pair of cameras, of a projector, and of a single glass interface that is shared between cameras and projector(s). The image formation model is based on the axial camera model. The proposed calibration uses a numerical optimization of a 3D cost function to determine all system parameters, thus avoiding the minimization of re-projection errors which require numerically solving a 12th order polynomial equation multiple times for each observed point. We also propose a novel stable approach to estimate the axis of the axial camera model. The proposed calibration was experimentally evaluated on four different glass interfaces, wherein several quantitative results were reported, including the re-projection error. The achieved mean angular error of the system's axis was under 6∘, and the mean absolute errors for the reconstruction of a flat surface were 1.38 mm for normal glass interfaces and 2.82 mm for the laminated glass interface, which is more than sufficient for application.

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features.

Versatile Video Coding (VVC) introduces many new coding technologies, such as quadtree with nested multi-type tree (QTMT), which greatly improves the efficiency of VVC coding. However, its computational complexity is higher, which affects the application of VVC in real-time scenarios. Aiming to solve the problem of the high complexity of VVC intra coding, we propose a low-complexity partition algorithm based on edge features. Firstly, the Laplacian of Gaussian (LOG) operator was used to extract the edges in the coding frame, and the edges were divided into vertical and horizontal edges. Then, the coding unit (CU) was equally divided into four sub-blocks in the horizontal and vertical directions to calculate the feature values of the horizontal and vertical edges, respectively. Based on the feature values, we skipped unnecessary partition patterns in advance. Finally, for the CUs without edges, we decided to terminate the partition process according to the depth information of neighboring CUs. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 54.08% of the encoding time on average, and the BDBR (Bjøntegaard delta bit rate) only increases by 1.61%.

Subjective and Objective Audio-Visual Quality Assessment for User Generated Content.

In recent years, User Generated Content (UGC) has grown dramatically in video sharing applications. It is necessary for service-providers to use video quality assessment (VQA) to monitor and control users' Quality of Experience when watching UGC videos. However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the perceptual quality also depends on the accompanying audio signals. In this paper, we conduct a comprehensive study on UGC audio-visual quality assessment (AVQA) from both subjective and objective perspectives. Specially, we construct the first UGC AVQA database named SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences collected from the YFCC100m database. A subjective AVQA experiment is conducted on the database to obtain the mean opinion scores (MOSs) of the A/V sequences. To demonstrate the content diversity of the SJTU-UAV database, we give a detailed analysis of the SJTU-UAV database as well as other two synthetically-distorted AVQA databases and one authentically-distorted VQA database, from both the audio and video aspects. Then, to facilitate the development of AVQA fields, we construct a benchmark of AVQA models on the proposed SJTU-UAV database and other two AVQA databases, of which the benchmark models consist of AVQA models designed for synthetically distorted A/V sequences and AVQA models built through combining the popular VQA methods and audio features via support vector regressor (SVR). Finally, considering benchmark AVQA models perform poorly in assessing in-the-wild UGC videos, we further propose an effective AVQA model via jointly learning quality-aware audio and visual feature representations in the temporal domain, which is seldom investigated by existing AVQA models. Our proposed model outperforms the aforementioned benchmark AVQA models on the SJTU-UAV database and two synthetically distorted AVQA databases. The SJTU-UAV database and the code of the proposed model will be released to facilitate further research.

Validation and enhancement of a vocal fold medial surface 3D reconstruction approach for in-vivo application.

In laryngeal research, studying the vertical vocal fold oscillation component is often disregarded. However, vocal fold oscillation by its nature is a three-dimensional process. In the past, we have developed an in-vivo experimental protocol to reconstruct the full, three-dimensional vocal fold vibration. The goal of this study is to validate this 3D reconstruction method. We present an in-vivo canine hemilarynx setup using high-speed video recording and a right-angle prism for 3D reconstruction of vocal fold medial surface vibrations. The 3D surface is reconstructed from the split image provided by the prism. For validation, reconstruction error was calculated for objects located at a distance of up to 15 mm away from the prism. The influence of camera angle, changing calibrated volume, and calibration errors were determined. Overall average 3D reconstruction error is low and does not exceed 0.12 mm at 5 mm distance from the prism. Influence of a moderate (5°) and large (10°) deviation in camera angle led to a slight increase in error to 0.16 mm and 0.17 mm, respectively. This procedure is robust towards changes in calibration volume and small calibration errors. This makes this 3D reconstruction approach a useful tool for the reconstruction of accessible and moving tissue surfaces.

Video-based digital platforms as an educational resource for the surgical preparation of orthopedic surgeons.

OBJECTIVE: The aim of the study was to research the video-based digital platforms that orthopedic specialists in Turkey use as an educational resource in their surgical preparations that they have not seen or done before, the frequency of their use of these platforms, and their trust in these platforms, with a survey study. METHODS: The importance of video-based digital platforms in surgical preparations that surgeons have not seen or done before was measured using the data obtained from 181 orthopedic specialists using a survey prepared on an Internet-based server (docs.google.com). RESULTS: Orthopedists used video-based digital platforms with a ratio of 38.7% among the educational resources in their surgical preparations that they have not seen or done before. There was no significant difference between the specialists with a surgical experience of 1-10 years and more than 10 years of experience in terms of using video-based digital platforms in surgical preparation (p>0.05). A total of 81.2% of the participants used only video-based digital platforms in the preparation of a surgical procedure they have never seen before. The most frequently used digital platform was YouTube, and 62% of the participants considered these platforms reliable. CONCLUSION: Orthopedic specialists in Turkey primarily and frequently use video-based digital platforms as a training resource in their preparations for surgery that they have not seen or done before. The establishment or support of platforms with evidence-based content with references from official orthopedic institutions and organizations can increase the trust of orthopedic specialists in these platforms.

Resident Education on the Surgical Treatment of Distal Radius Fractures Using a Point-of-View Camera.

Background: In the coronavirus disease 2019 (COVID-19) era, surgical resident education depends largely on virtual materials. With the help of point-of-view (POV) cameras, educational videos have become widely used for surgical training. A video recorded from the surgeon's POV helps demonstrate the procedure. We made training movies of the surgical approach to distal radius fractures for residents using a head-mounted video recording system with a laser point targeting device (LPTD). Methods: A 15-minnute movie of the trans-flexor carpi radialis approach for distal radius fractures was made. A POV camera was assembled with an LPTD and strapped on the surgeon's head. This enabled maintenance of the surgical field while recording the procedure. A shorter version of the clip was also made to investigate trainee preference. We asked 24 trainees to watch the two versions of the video and complete a short questionnaire. Results: All trainees felt that the movie made with a POV camera was more efficient than existing materials. Only 1 (4.2%) felt that the laser pointer hindered the view. Four of the 23 trainees (16.7%) felt dizzy while watching the video. Of the two versions, 16 trainees (66.7%) preferred the shorter, edited version. The average score for the video was 8.42 out of 10. Conclusions: A video recording system in the operating room that uses an LPTD-POV camera is an efficient way to produce educational material, particularly for surgical residents during the COVID-19 era.