Surgical Video Research Articles

A systematic scoping review protocol to summarise and appraise the use of artificial intelligence in the analysis of digital videos of invasive general surgical procedures.

Intraoperative video recordings are a valuable addition to operative written documentation. However, the review of these videos often requires surgical expertise and takes considerable time. While a large amount of work has been undertaken to understand the role of artificial intelligence (AI) in healthcare more generally, the application of these techniques to automate the analysis of surgical videos is currently unclear. In this systematic scoping review, we sought to give a contemporary overview of the use of AI research in the analysis of digital videos of invasive general surgical procedures. We will describe and summarise the study characteristics, purpose of the applications and stage of development, to ascertain how these techniques might be applied in future research and to identify gaps in current knowledge (e.g. uncertainties about the study methods). Systematic searches will be conducted in OVID Medline and Embase, using terms related to 'artificial intelligence', 'surgery' and 'video' to identify all potentially relevant studies published since 1st January 2012. All primary studies where AI has been applied to the analysis of videos (recorded by conventional digital cameras or laparoscopic or robotic-assisted technology) of general surgical procedures will be included. Data extraction will include study characteristics, governance, details of video datasets and AI models, measures of accuracy, validation and any reported limitations. No ethical approval is required as primary data will not be collected. The results will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal.

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images

The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care. The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average. The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation. As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.

Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training

Background and ObjectiveSafety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. MethodsIn this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. ResultsOur network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. ConclusionsOur network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.

Nonlinear regression of remaining surgery duration from videos via Bayesian LSTM-based deep negative correlation learning

In this paper, we address the problem of estimating remaining surgery duration (RSD) from surgical video frames. We propose a Bayesian long short-term memory (LSTM) network-based Deep Negative Correlation Learning approach called BD-Net for accurate regression of RSD prediction as well as estimation of prediction uncertainty. Our method aims to extract discriminative visual features from surgical video frames and model the temporal dependencies among frames to improve the RSD prediction accuracy. To this end, we propose to train an ensemble of Bayesian LSTMs on top of a backbone network by the way of deep negative correlation learning (DNCL). More specifically, we deeply learn a pool of decorrelated Bayesian regressors with sound generalization capabilities through managing their intrinsic diversities. BD-Net is simple and efficient. After training, it can produce both RSD prediction and uncertainty estimation in a single inference run. We demonstrate the efficacy of BD-Net on publicly available datasets of two different types of surgeries: one containing 101 cataract microscopic surgeries with short durations and the other containing 80 cholecystectomy laparoscopic surgeries with relatively longer durations. Experimental results on both datasets demonstrate that the proposed BD-Net achieves better results than the state-of-the-art (SOTA) methods. A reference implementation of our method can be found at: https://github.com/jywu511/BD-Net.

OGC P68 Surgical performance of Minimally Invasive Esophagectomy is associated with patient outcome on a national level - A video assessment study

Abstract Background It is highly likely that suboptimal surgical performance is associated with less favorable patient outcomes in minimally invasive esophagectomy (MIE). Establishing this association can lead to programs that promote better surgical performance of MIE and improve patient outcomes. Methods In this nationwide cohort study, 7 blinded expert MIE surgeons assessed performance of surgical videos of MIE performed in 2022, using a previously developed and validated competency assessment tool (CAT), the MIE-CAT. Hospitals were divided into quartiles based on their MIE-CAT score. Patient outcomes of 2021-2022 registered in the Dutch Upper-GI Clinical Audit were compared between highest (top 25%) and lowest (bottom 25%) performing quartiles. Multilevel logistic regression, with clustering of patients within hospitals was used to analyze associations between performance and outcomes, and to correct for casemix. Primary outcome parameter was severe postoperative complications (Clavien-Dindo≥3) within 30 days after surgery. Results MIE-CAT scores ranged from range 93.9-118.8. In total, 970 patients were included. Severe postoperative complications occurred in 18.7% (n=41/219) in the highest performing quartile versus 39.2% (n=40/102) in the lowest performing quartile (RR=0.50, 95%CI 0.24-0.99). Moreover, there were less conversions (1.8% versus 8.9%, RR=0.21, 95%CI 0.21-0.21), less peroperative complications (2.7% versus 7.8%, RR=0.21, 95%CI 0.04-0.94) and overall less postoperative complications (46.1% versus 65.7%, RR=0.54, 95%CI 0.24-0.96). Oncological outcomes R0 resection and lymph node yield increased with oncologic specific-performance scores (e.g. hiatus dissection/thoracic esophagus dissection and abdominal/thoracic lymph node dissection respectively). ). A strong association between high anastomotic-phase score and a lower anastomotic leakage rate was observed (4.6% versus 17.7%, RR=0.14, 95%CI 0.06-0.31). Conclusions Better surgical performance was associated with decreased perioperative complications and better oncological outcome for esophageal cancer patients undergoing MIE. If surgical performance of MIE can be improved, significantly better patient outcomes may be possible.

Video-based Coaching: Current Status and Role in Surgical Practice, Part II- Practical Implementation and Risk Mitigation from the Society for Surgery of the Alimentary Tract, Health Care Quality and Outcomes Committee

IntroductionVideo-based surgical coaching is gaining traction within the surgical community. It has an increasing adoption rate and growing recognition of its utility, especially an advanced continuous professional growth tool, for continued educational purposes. This method offers instructional flexibility in real-time remote settings and asynchronous feedback scenarios. In our first paper, we delineated fundamental principles for video-based coaching, emphasizing the customization of feedback to suit individual surgeon’s needs. MethodIn this second part of the series, we review into practical applications of video-based coaching, focusing on quality improvements in a team-based setting, such as the trauma bay. Additionally, we address the potential risks associated with surgical video recording, storage, and distribution, particularly regarding medicolegal aspects. We propose a comprehensive framework to facilitate the implementation of video coaching within individual healthcare institutions. ResultsOur paper examines the legal and ethical framework and explores the potential benefits and challenges, offering insights into the real-world implications of this educational approach. ConclusionThis paper contributes to the discourse on integrating video-based coaching into continuous professional development. It aims to facilitate informed decision-making in healthcare institutions, considering the adoption of this innovative educational quality tool.

Basal Cell Carcinoma Excision from the Lower Lip with Versatile Keystone Flap for Vascularized Skin Replacement

Watch this full-length, narrated surgical video of a basal cell carcinoma excision with keystone flap reconstruction. This was performed by Dr. Geoffrey Hallock during a surgical mission in Honduras with the World Surgical Foundation.

Bone Graft for Nonunion of Right Thumb Proximal Phalanx Fracture

Watch this full-length, narrated surgical video of a bone graft for a non-union of a right thumb proximal phalanx fracture. This was performed during a surgical mission in Honduras with the World Surgical Foundation.

Artificial intelligence for automatic surgical phase recognition of laparoscopic gastrectomy in gastric cancer.

This study aimed to classify laparoscopic gastric cancer phases. We also aimed to develop a transformer-based artificial intelligence (AI) model for automatic surgical phase recognition and to evaluate the model's performance using laparoscopic gastric cancer surgical videos. One hundred patients who underwent laparoscopic surgery for gastric cancer were included in this study. All surgical videos were labeled and classified into eight phases (P0. Preparation. P1. Separate the greater gastric curvature. P2. Separate the distal stomach. P3. Separate lesser gastric curvature. P4. Dissect the superior margin of the pancreas. P5. Separation of the proximal stomach. P6. Digestive tract reconstruction. P7. End of operation). This study proposed an AI phase recognition model consisting of a convolutional neural network-based visual feature extractor and temporal relational transformer. A visual and temporal relationship network was proposed to automatically perform accurate surgical phase prediction. The average time for all surgical videos in the video set was 9114 ± 2571s. The longest phase was at P1 (3388s). The final research accuracy, F1, recall, and precision were 90.128, 87.04, 87.04, and 87.32%, respectively. The phase with the highest recognition accuracy was P1, and that with the lowest accuracy was P2. An AI model based on neural and transformer networks was developed in this study. This model can identify the phases of laparoscopic surgery for gastric cancer accurately. AI can be used as an analytical tool for gastric cancer surgical videos.

Infusion-Assisted Evacuation of Submacular Hemorrhage.

To report a novel surgical technique for evacuating submacular hemorrhage using the infusion stream of a 25 gauge vitrectomy system. Surgical case and video. A 54 year old male was taken to the operating room for a total hyphema, non-clearing vitreous hemorrhage, and elevated intraocular pressure after multiple tractional retinal detachment repairs by an outside surgeon. Intraoperatively the hyphema and vitreous hemorrhage were cleared, and the source of bleeding was discovered to be an avulsed vessel through a pre-existing retinal break just superior to the optic nerve. A large submacular hemorrhage was also present that was unable to be drained through the break by aspiration alone. With the vitreous cavity under fluid, the infusion cannula was manipulated to guide the infusion stream onto the macula. The stream was directed in a distal to proximal manner towards the retinal break, and the submacular blood was successfully expressed out through the break. Postoperatively the retina remained attached with almost complete resolution of the submacular hemorrhage. Utilizing the mechanical pressure of the infusion stream can be an effective method for evacuating large subretinal hemorrhages.

Intraobserver and Interobserver Reliability of the Snyder and Expanded SLAP Classification System: A Video Study

Background: Superior labral anterior and posterior (SLAP) tears are a common finding in overhead athletes. The original classification system produced by Snyder in 1990 contained 4 types of SLAP tears and was later expanded to 10 types. The classification has been challenging because of inconsistencies between surgeons making diagnoses and treatments based on the diagnosis. Furthermore, patient factors—such as age and sports played—affect the treatment algorithms, even across similarly classified SLAP tears. Purpose: To (1) assess the interobserver and intraobserver reliability of the Snyder and expanded SLAP (ESLAP) classification systems and (2) determine the consistency of treatment for a given SLAP tear depending on different clinical scenarios. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A total of 20 arthroscopic surgical videos and magnetic resonance imaging scans of patients with SLAP tears were sent to 20 orthopaedic sports medicine surgeons at various stages of training. Surgeons were asked to identify the type of SLAP tear using the Snyder and ESLAP classifications. Surgeons were then asked to determine the treatment for a SLAP tear using 4 clinical scenarios: (1) in the throwing arm of an 18-year-old pitcher; (2) in the dominant arm of an 18-year-old overhead athlete; (3) a 35-year-old overhead athlete; (4) or a 50-year-old overhead athlete. Responses were recorded, and the cases were shuffled and sent back 6 weeks after the initial responses. Results were then analyzed using the Fleiss kappa coefficient (κ) to determine interobserver and intraobserver degrees of agreement. Results: There was moderate intraobserver reliability in both the Snyder and ESLAP classifications (κ = 0.52) and fair interobserver reliability for both classification systems (Snyder, κ = 0.31; ESLAP, κ = 0.30; P < .0001) among all surgeons. Additionally, there was only fair agreement (κ = 0.30; P < .0001) for the treatment modalities chosen by the reviewers for each case. Conclusion: This study demonstrated that SLAP tears remain a challenging problem for orthopaedic surgeons in diagnostics and treatment plans. Therefore, care should be taken in the preoperative discussion with the patient to consider all the possible treatment options because this may affect the postoperative recovery period and patient expectations.

LAST: LAtent Space-constrained Transformers for Automatic Surgical Phase Recognition and Tool Presence Detection.

When developing context-aware systems, automatic surgical phase recognition and tool presence detection are two essential tasks. There exist previous attempts to develop methods for both tasks but majority of the existing methods utilize a frame-level loss function (e.g., cross-entropy) which does not fully leverage the underlying semantic structure of a surgery, leading to sub-optimal results. In this paper, we propose multi-task learning-based, LAtent Space-constrained Transformers, referred as LAST, for automatic surgical phase recognition and tool presence detection. Our design features a two-branch transformer architecture with a novel and generic way to leverage video-level semantic information during network training. This is done by learning a non-linear compact presentation of the underlying semantic structure information of surgical videos through a transformer variational autoencoder (VAE) and by encouraging models to follow the learned statistical distributions. In other words, LAST is of structure-aware and favors predictions that lie on the extracted low dimensional data manifold. Validated on two public datasets of the cholecystectomy surgery, i.e., the Cholec80 dataset and the M2cai16 dataset, our method achieves better results than other state-of-the-art methods. Specifically, on the Cholec80 dataset, our method achieves an average accuracy of 93.12±4.71%, an average precision of 89.25±5.49%, an average recall of 90.10±5.45% and an average Jaccard of 81.11 ±7.62% for phase recognition, and an average mAP of 95.15±3.87% for tool presence detection. Similar superior performance is also observed when LAST is applied to the M2cai16 dataset.

Combined endoscopic endonasal and transcranial approach for internal carotid artery aneurysms: usefulness and safety of endonasal proximal control.

It is necessary to secure both the proximal and distal sides of the parent artery to prevent premature rupture when clipping cerebral aneurysms. Herein, we describe four cases in which the proximal internal carotid artery (ICA), affected by a paraclinoid aneurysm, was secured using an endoscopic endonasal approach. We used various tools, including a surgical video, cadaver dissection picture, artist's illustration, and intraoperative photographs, to elucidate the process. No patient experienced postoperative complications at our institution. Compared to the cervical or cavernous ICA, the ICA adjacent to the clivus (paraclival ICA) can be anatomically safely and easily exposed using an endoscopic endonasal approach because there is no need to consider cerebrospinal fluid leakage or hemorrhage from the cavernous sinus. Securing the proximal side of the parent artery using an endoscopic endonasal approach may be a viable method for clipping selected ICA aneurysms, such as paraclinoid aneurysms especially for upward or outward aneurysms of the C2 portion.

Partial Labyrinth Removal Restricted to the Superior Semicircular Canal in Focal Combined Transpetrosal Approach: Description and Illustrative Cases.

The labyrinthine structures obstruct the surgical view of the deep petroclival region in the transpetrosal approach. Historically, labyrinthectomy and removal of all 3 semicircular canals, with resultant deafness, was used in patients with ipsilateral functional hearing deficits to improve access. The advent and systematization of superior and posterior semicircular canal removal (transcrusal approach) with good rates of hearing preservation has allowed a redefinition of the possibility of partial labyrinthectomy in patients without previous hearing deficits. The present manuscript is intended to describe a technical refinement of partial labyrinthectomy during focal combined petrosectomy, offering a customization of the approach through the selective removal of the superior semicircular canal for specific types of tumors. The use of the technique is demonstrated through surgical drawings, pictures, and videos. The rationale to indicate this new approach is discussed based on clinical cases. Three illustrative clinical cases (petroclival meningiomas) are demonstrated. Functional hearing on the approach side has been preserved in all of them. The focal combined transpetrosal approach associated with the superior semicircular canal resection has been a promising surgical technique in the treatment of selected petroclival tumors. It has the potential to further decrease the risks of postoperative auditory and vestibular dysfunctions associated with labyrinthectomies.

Macular Hole Hydrodissection Technique with Human Amniotic Membrane for Repair of Large Macular Holes.

To describe a combined surgical technique utilizing the Macular Hole Hydrodissection (MHH) with human amniotic membrane (hAM) for repair of large macular holes. A step-by-step procedure and a surgical video using the combined MHH and hAM technique are presented. As the first step, the MHH separates the adhesions of the macular hole to the underlying retinal pigment epithelium (RPE) with a soft tipped cannula through proportional reflux followed by gentle passive aspiration. The hAM graft is marked to identify the non-sticky epithelial side and ensure that the stromal layer (sticky and non-shinny) is facing downward towards the RPE. The graft is then tucked into the space created with MHH between the macular hole edges and the RPE with closed forceps to decrease the likelihood of the graft from dislocating post-operatively. The MHH in combination with the hAM is a practical and effective technique for addressing challenging large macular holes.

Validation of a low-cost model for training laparoscopic ovary-sparing surgery (LOSS) in ovarian mature teratomas

BackgroundMature ovarian teratoma is the most common ovarian neoplasm in pediatrics. Laparoscopic ovary sparing surgery (LOSS) is effective and safe. Due to the expansion of this technique in recent years, education-based surgical simulation is a useful tool for skills development. AimTo validate a low-cost model for training LOSS in pediatric ovarian teratomas. MethodsWe developed a low-cost model, incorporated into Hands-On courses since 2018. After academic lectures, and surgical strategies video demonstration, all participants performed the procedure in pairs, in our pediatric gruyer-like-training-box, under supervision. Time and critical maneuvers were assessed. A Likert-type scale was used to evaluate: previous experience, anatomical appearance of the model, surgical anatomy compared to a real patient and utility as a training method. Statistical analysis was performed with R-Studio®. Results75 % considered the model as good or high degree of similarity in relation to the anatomical characteristics; and 91.3 % in relation to surgical anatomy. 78.8 % believe the model improves the majority of skills. Correlation in time assessment for 81 participants with different levels of experience was statistically significative (p<0.01*). Emphasis was placed on preserving the capsule and ovarian tissue without causing any injury to adjacent tissues, resulting in a homogeneous performance. ConclusionWe saw a correlation between the previous experience of the surgeon and their performance in the model. Introducing this model in a simulation curricula would improve the learning curve. A decrease in surgical technique complications is to be expected, and should be demonstrated in future studies.

Developing the surgeon-machine interface: using a novel instance-segmentation framework for intraoperative landmark labelling.

The utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset. Our custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames. Our SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance -our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios. We present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. The deep learning model systems can be utilized in clinical applications via data expansion.

Can YouTube Be Considered a Reliable Learning Tool for Urology Trainees Seeking Information on Testicular Exploration?

Introduction YouTube, the world's largest video platform, hosts thousands of educational surgical videos that many trainees rely on to enhance their understanding and proficiency in various surgical procedures. Consequently, a crucial inquiry arises regarding the trustworthiness of these videos as a valuable resource for these trainees. In this article, we address this question by focusing on one of the most frequently performed surgical procedures in the field of urology and assessing the effectiveness of these videos as an educational tool for urology trainees (ST3+:Specialty Training Year 3 and above). Methodology We conducted a comprehensive search on YouTube for all videos related to 'Testicular Exploration'. After applying specific inclusion and exclusion criteria, we identified a total of nine eligible videos for analysis. These videos were assessed using the LAParoscopic Surgery Video Educational GuidelineS (LAP-VEGaS) scoring system, which categorized them into two distinct groups. The first group, known as the 'high-quality group', included videos that scored 11 points or higher according to the LAP-VEGaS scoring criteria. The second group, termed the 'low-quality group', consisted of videos that scored less than 11 points using the LAP-VEGaS scoring tool. Additionally, we collected data on various metrics, such as video view counts, duration, likes and dislikes counts, comments count, like ratio, view ratio, and power index, and performed a comparative analysis between the two aforementioned groups. Results Between April 2013 and September 2023, the selected videos exhibited an average total view count of 95,546±138,000. The videos had an average duration of 6.35±2.26 minutes. Furthermore, the mean values for both likes and dislikes were 461.55±581 and 2.89±2.86, respectively. In contrast, the mean like ratio, view ratio, and power index were 0.98±0.0112, 176,00±13,100, and 173.80±131, respectively. The mean LAP-VEGaS scores for videos related to testicular exploration were 9.94±2.05. It is noteworthy that the first group had a statistically higher number of dislikes; however, the view count, comments count, likes count, and view ratio were statistically lower in the same group. Conclusion Videos related to testicular exploration on YouTube exhibit notably low quality and do not serve as a valuable resource for urology trainees. Key factors such as video duration, total view count, and viewer interactions (including likes, dislikes, and comments) should not be relied upon as indicators of educational video quality. Consequently, it is advisable for urology trainees to refrain from using YouTube as a primary source for learning about testicular exploration. Instead, they should seek guidance and support from experienced senior colleagues, educational supervisors, or consultants to explore more reliable sources of information for this surgical procedure.

Clipping of Bilateral Ophthalmic Artery Aneurysms Through a Single Craniotomy.

Bilateral ophthalmic aneurysms are rare and involve two aneurysms in the ophthalmic arteries, one on each, leading to potential symptoms such as vision loss and headaches. The treatment options for aneurysms, ranging from surgery and endovascular embolization to observation, depend on various factors, including aneurysm size and the patient's health.Microsurgery, while presenting complexities due to the intricate anatomy of the anterior clinoid region, offers potential advantages such as enhanced decompression rates and reduced aneurysm recurrence. The presented surgical video illustrates the treatment of bilateral ophthalmic artery aneurysms via a single craniotomy. This method reduces surgical duration and trauma, facilitating quicker patient recovery. However, this method bears potential risks, especially to both optic nerves. As underscored in the video, the utmost anatomical understanding in the anterior clinoid area is pivotal for successful outcomes and reduced complications.