BackgroundDeep learning (DL) methods for surgical video analysis have expanded rapidly in minimally invasive surgery (MIS). However, a structured bibliometric overview focused on DL-based surgical instrument segmentation, detection, and tracking is lacking. The objective of this review is to systematically map the research landscape with this focus, by examining publication trends, influential authors, institutions, and countries, collaboration networks, keyword co-occurrence patterns, and the thematic trajectory of the discipline.MethodsWe performed a bibliometric analysis of original research articles on DL-based surgical instrument segmentation/detection/tracking in laparoscopic or robotic MIS, published between 2017 and 2024. Searches were conducted in six databases namely PubMed, Scopus, IEEE Xplore, Embase, Medline, and Web of Science. Records were de-duplicated in EndNote and analyzed using the Bibliometrix R package, with co-authorship, co-citation, and keyword networks visualized in VOSviewer. Citation counts were extracted from each study’s respective database and interpreted cautiously given the influence of publication age.ResultsWe included 217 articles. Annual output increased from 2017 to a peak in 2023, indicating sustained growth in DL research for MIS instrument analysis. The most productive countries included the United States and France, with major institutional contributions from the University of Strasbourg and Furtwangen University. Keyword analysis indicated continued dominance of convolutional neural networks alongside emerging themes including transformer-based architectures, multimodal learning, and real-time intraoperative applications.ConclusionsThis bibliometric study characterizes the evolution, leading contributors, collaboration patterns, and thematic trajectories of DL-based instrument segmentation/detection/tracking in MIS. While these findings can inform research prioritization and collaboration, this study does not evaluate clinical effectiveness. Future work should prioritize explainable and efficient real-time models, standardized annotation protocols, and broader global partnerships to support responsible clinical translation.
Real-world federated learning for brain imaging scientists
BackgroundFederated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We