ArticlemedRxiv : the preprint server for health sciences2025
Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
Background: The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational tools for intraoperative surgical decision support. Surgical procedures are not routinely recorded, and annotation requires domain expertise, resulting in a scarcity of open-access surgical video datasets with high-quality annotations. Existing datasets are typically limited to single institutions and specific procedures, such as cholecystectomy, and rarely comprise patient-level metadata like demographic characteristics, disease history, or laboratory parameters. Methods: The Appendix300 dataset comprises 330 laparoscopic surgery recordings, including 325 full-length laparoscopic appendectomies and 5 control recordings from non-appendectomy procedures in pediatric and adult patients treated at five German centers. The dataset includes patient-level clinical metadata (demographics, medical history, clinical symptoms, laboratory parameters, and histopathological findings), as well as standardized expert annotations of the laparoscopic grade of appendicitis. Results: Appendix300 currently represents the largest publicly available collection of surgical video data with patient metadata and the first curated dataset of laparoscopic appendectomies. It enables novel validation tasks for computer vision in surgery, including the classification of appendicitis severity and the detection of appendiceal perforation. Technical validation of the laparoscopic appendicitis grade annotations showed substantial interrater agreement (weighted Cohen's κ = 0.615). Conclusion: The Appendix300 dataset expands the scope of surgical data science by integrating video data with clinical and pathological metadata across institutions. It enables new and clinically relevant patient-level validation tasks for computer vision in laparoscopic surgery and facilitates decentralized learning approaches, overall enhancing the breadth and translational relevance of AI-based surgical video analysis.
Identifiers
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.