ReviewFrontiers in digital health2026
Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Deep 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. Methods: We 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. Results: We 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. Conclusions: This 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.