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Detecting surgical tools is an essential task for analyzing and evaluating surgical videos. However, most studies focus on minimally invasive surgery (MIS) and cataract surgery. Mainly because of a lack of a large, diverse, and well-annotated dataset, research in the area of open surgery has been limited so far. Open surgery video analysis is challenging because of its properties: varied number and roles of people (e.g., main surgeon, assistant surgeons, and nurses), a complex interaction of tools and hands, various operative environments, and lighting conditions. In this paper, to handle these limitations and difficulties, we introduce an egocentric open surgery dataset that includes 15 open surgeries recorded with a head-mounted camera. More than 67k bounding boxes are labeled to 19k images with 31 surgical tool categories. Finally, we present a surgical tool detection baseline model based on recent advances in object detection. The results of our new dataset show that our presented dataset provides enough interesting challenges for future methods and that it can serve as a strong benchmark to address the study of tool detection in open surgery.

BackgroundAnnotated data are foundational to applications of supervised machine learning. However, there seems to be a lack of common language used in the field of surgical data science.The aim of this study is to review the process of annotation and semantics used in the creation of SPM for minimally invasive surgery videos.MethodsFor this systematic review, we reviewed articles indexed in the MEDLINE database from January 2000 until March 2022. We selected articles using surgical video annotations to describe a surgical process model in the field of minimally invasive surgery. We excluded studies focusing on instrument detection or recognition of anatomical areas only. The risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data from the studies were visually presented in table using the SPIDER tool.ResultsOf the 2806 articles identified, 34 were selected for review. Twenty-two were in the field of digestive surgery, six in ophthalmologic surgery only, one in neurosurgery, three in gynecologic surgery, and two in mixed fields. Thirty-one studies (88.2%) were dedicated to phase, step, or action recognition and mainly relied on a very simple formalization (29, 85.2%). Clinical information in the datasets was lacking for studies using available public datasets. The process of annotation for surgical process model was lacking and poorly described, and description of the surgical procedures was highly variable between studies.ConclusionSurgical video annotation lacks a rigorous and reproducible framework. This leads to difficulties in sharing videos between institutions and hospitals because of the different languages used. There is a need to develop and use common ontology to improve libraries of annotated surgical videos.

BackgroundRoutine surgical video recording has multiple benefits. Video acts as an objective record of the operative record, allows video-based coaching and is integral to the development of digital technologies. Despite these benefits, adoption is not widespread. To date, only questionnaire studies have explored this failure in adoption. This study aims to determine the barriers and provide recommendations for the implementation of routine surgical video recording.Materials and methodsA pre- and post-pilot questionnaire surrounding a real-world implementation of a C-SATS©, an educational recording and surgical analytics platform, was conducted in a university teaching hospital trust. Usage metrics from the pilot study and descriptive analyses of questionnaire responses were used with the non-adoption, abandonment, scale-up, spread, sustainability (NASSS) framework to create topic guides for semi-structured interviews. Transcripts of interviews were evaluated in an inductive thematic analysis.ResultsEngagement with the C-SATS© platform failed to reach consistent levels with only 57 videos uploaded. Three attending surgeons, four surgical residents, one scrub nurse, three patients, one lawyer, and one industry representative were interviewed, all of which perceived value in recording. Barriers of ‘change,’ ‘resource,’ and ‘governance,’ were identified as the main themes. Resistance was centred on patient misinterpretation of videos. Participants believed availability of infrastructure would facilitate adoption but integration into surgical workflow is required. Regulatory uncertainty was centred around anonymity and data ownership.ConclusionBarriers to the adoption of routine surgical video recording exist beyond technological barriers alone. Priorities for implementation include integration recording into the patient record, engaging all stakeholders to ensure buy-in, and formalising consent processes to establish patient trust.

BackgroundThe growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration.MethodsFour working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups.ResultsAfter three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established.ConclusionsWhile additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.

PurposeThe aim of this review was to collate current evidence wherein digitalisation, through the incorporation of video technology and artificial intelligence (AI), is being applied to the practice of surgery. Applications are vast, and the literature investigating the utility of surgical video and its synergy with AI has steadily increased over the last 2 decades. This type of technology is widespread in other industries, such as autonomy in transportation and manufacturing.MethodsArticles were identified primarily using the PubMed and MEDLINE databases. The MeSH terms used were “surgical education”, “surgical video”, “video labelling”, “surgery”, “surgical workflow”, “telementoring”, “telemedicine”, “machine learning”, “deep learning” and “operating room”. Given the breadth of the subject and the scarcity of high-level data in certain areas, a narrative synthesis was selected over a meta-analysis or systematic review to allow for a focussed discussion of the topic.ResultsThree main themes were identified and analysed throughout this review, (1) the multifaceted utility of surgical video recording, (2) teleconferencing/telemedicine and (3) artificial intelligence in the operating room.ConclusionsEvidence suggests the routine collection of intraoperative data will be beneficial in the advancement of surgery, by driving standardised, evidence-based surgical care and personalised training of future surgeons. However, many barriers stand in the way of widespread implementation, necessitating close collaboration between surgeons, data scientists, medicolegal personnel and hospital policy makers.

BackgroundSurgical video recording provides the opportunity to acquire intraoperative data that can subsequently be used for a variety of quality improvement, research, and educational applications. Various recording devices are available for standard operating room camera systems. Some allow for collateral data acquisition including activities of the OR staff, kinematic measurements (motion of surgical instruments), and recording of the endoscopic video streams. Additional analysis through computer vision (CV), which allows software to understand and perform predictive tasks on images, can allow for automatic phase segmentation, instrument tracking, and derivative performance-geared metrics. With this survey, we summarize available surgical video acquisition technologies and associated performance analysis platforms.MethodsIn an effort promoted by the SAGES Artificial Intelligence Task Force, we surveyed the available video recording technology companies. Of thirteen companies approached, nine were interviewed, each over an hour-long video conference. A standard set of 17 questions was administered. Questions spanned from data acquisition capacity, quality, and synchronization of video with other data, availability of analytic tools, privacy, and access.ResultsMost platforms (89%) store video in full-HD (1080p) resolution at a frame rate of 30 fps. Most (67%) of available platforms store data in a Cloud-based databank as opposed to institutional hard drives. CV powered analysis is featured in some platforms: phase segmentation in 44% platforms, out of body blurring or tool tracking in 33%, and suture time in 11%. Kinematic data are provided by 22% and perfusion imaging in one device.ConclusionVideo acquisition platforms on the market allow for in depth performance analysis through manual and automated review. Most of these devices will be integrated in upcoming robotic surgical platforms. Platform analytic supplementation, including CV, may allow for more refined performance analysis to surgeons and trainees. Most current AI features are related to phase segmentation, instrument tracking, and video blurring.

Purpose This study aims to assess the quality of educational surgical videos for robotic total mesorectal excision (TME), across widely used open-source platforms, using a newly designed quality assessment checklist. Methods The checklist was developed by using existing society guidelines, such as the European Academy of Robotic Colorectal Surgery, comprising four key sections: (i) usability of the platform, (ii) video component, (iii) intraoperative techniques and (iv) other information (including case presentation and outcomes). Videos were identified using the search terms ‘Robotic TME’ from surgical education platforms (WebSurg, C-SATS and Touch Surgery) and YouTube, between January 2016 and July 2024. All videos displaying robotic TME were reviewed and scored using the quality assessment tool (/12), and the videos across the platforms were subsequently compared. Results A total of 113 videos were scored using the checklist: 63 surgical education platform (10 WebSurg and 53 C-SATS) and 50 YouTube videos. The total median checklist score achieved by WebSurg (9 [IQR 8–9] and YouTube videos (8 [IQR 7–10]) was significantly higher than CSAT-S videos (4 [IQR 4–5]; p  < 0.001). The usability of platform scores for YouTube was significantly higher than WebSurg and C-SATS videos ( p  < 0.001). Scores for video components, intraoperative techniques and other information were higher across WebSurg and YouTube videos when compared to C-SATS ( p  < 0.001); however, there was no significant difference between WebSurg and YouTube for each domain. Conclusion The overall educational quality of online robotic TME videos was found to be generally heterogeneous, with WebSurg and YouTube videos demonstrating higher scores based on the checklist. A new quality assessment tool has been proposed for robotic TME videos, which has the potential to improve the reliability and value of published video research.

Annotation of surgical video is important for establishing ground truth in surgical data science endeavors that involve computer vision. With the growth of the field over the last decade, several challenges have been identified in annotating spatial, temporal, and clinical elements of surgical video as well as challenges in selecting annotators. In reviewing current challenges, we provide suggestions on opportunities for improvement and possible next steps to enable translation of surgical data science efforts in surgical video analysis to clinical research and practice.

The Thoracic Training Course of the Royal College of Surgeons of Edinburgh in China is dedicated to training skilled thoracic surgeons to an elite level. Almost all the learners at the Nanjing training station showed great interest in a novel stapler tractor designed for dissociating segmental vessels during lung segmentectomy. This study aimed to unveil the beneficial of the novel stapler tractor. Three hundred twenty-three patients who underwent lung segmentectomy at the Nanjing training station were retrospectively analyzed. First, surgical outcomes were compared between those who used a stapler tractor for dissociating segmental vessels and those who did not. Secondly, the results of dissociating segmental vessels were compared between the group that used the stapler tractor and the other three techniques. Lastly, the same variable comparisons were made among the learners. Compared with the outcomes of the non-used stapler tractor patient group during the teaching period, the patients in the used stapler tractor group had shorter operation times (143.15 ± 28.05 min vs. 152.83 ± 37.92 min, P = 0.019), less intraoperative bleeding (51.41 ± 42.60 mL vs. 70.70 ± 63.19 mL, P = 0.017), shorter postoperative hospital stays (3.39 ± 0.81 days vs. 3.70 ± 1.07 days, P = 0.008), and a lower incidence of postoperative pulmonary embolism (0.3% vs. 4.2%, P = 0.035). Compared with the results of other techniques for dissociating segmental vessels, the stapler tractor group had a lower occurrence of intraoperative bleeding (P = 0.002), a reduced time for dissociating the vessel (P < 0.05), a greater successful traction ratio (P = 0.022), and a shorter vascular stump length (P = 0.000). The learners reproduced similar outcomes and results in 58 patients across different affiliations. The benefits of using stapler techniques for dissociating vascular structures in lung segmentectomy include, but are not limited to, shortened operation time, reduced intraoperative blood loss, shorter postoperative hospital stays, reduced postoperative air leakage rates, and a lower incidence of postoperative pulmonary embolism, whether during the teaching or learning period.

Artificial intelligence has shown great promise in advancing intelligent surgical systems. Among its applications, surgical video action recognition plays a critical role in enabling accurate intraoperative understanding and decision support. However, the task remains challenging due to the temporal continuity of surgical scenes and the long-tailed, semantically entangled distribution of action triplets composed of instruments, verbs, and targets. To address these issues, we propose TriQuery, a query-based model for surgical triplet recognition and classification. Built on a multi-task Transformer framework, TriQuery decomposes the complex triplet task into three semantically aligned subtasks using task-specific query tokens, which are processed through specialized attention mechanisms. We introduce a Multi-Query Decoding Head (MQ-DH) to jointly model structured subtasks and a Top-K Guided Query Update (TKQ) module to incorporate inter-frame temporal cues. Experiments on the CholecT45 dataset demonstrate that TriQuery achieves improved overall performance over existing baselines across multiple classification tasks. Attention visualizations further show that task queries consistently attend to semantically relevant spatial regions, enhancing model interpretability. These results highlight the effectiveness of TriQuery for advancing surgical video understanding in clinical environments.