ArticleScientific data2025
LapEx: A new multimodal dataset for context recognition and practice assessment in laparoscopic surgery.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- PituPhase65: An endoscopic pituitary surgery dataset for surgical phase recognition.Scientific data · 2026Article
- Multimodal machine learning for staging laparoscopy: a combined image analysis and morphologic tool for the discrimination of peritoneal metastasis.International journal of surgery (London, England) · 2026Article
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- LapEx: A new multimodal dataset for context recognition and practice assessment in laparoscopic surgery.Scientific data · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Surgical Data Science (SDS), there is an increasing demand for large, realistic annotated datasets to facilitate the development of machine learning techniques. However, in laparoscopic surgery, most publicly available datasets focus on low-granularity procedural annotations (such as phases or steps) and image segmentation of instruments or specific organs, often using animal models that lack clinical realism. Furthermore, annotation variability is seldom evaluated. In this work, we compiled 30 sleeve gastrectomy procedures and performed three levels of annotations for a specific step of this procedure (the fundus dissection): a procedural annotation of fine-grained activities, a semantic segmentation of the laparoscopic images, and the assessment of a surgical skill, specifically the quality of exposition of the surgical scene. We also conducted a comprehensive annotation variability analysis, highlighting the complexity of these tasks and providing a baseline for evaluating machine learning models. The dataset is publicly available and serves as a valuable resource for advancing SDS research.
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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.