Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions

Clinical Snapshot

Key Findings

• P Value: Not applicable for dataset description

• Effect Size: 325 full-length appendectomies plus 5 control procedures

• Primary Outcome: Creation of comprehensive surgical video dataset with 330 laparoscopic recordings

• Nnt Or Sensitivity: Dataset enables AI model development with potential for improved surgical decision support

• Confidence Interval: Not applicable for dataset description

Clinical Application

High feasibility for research applications, requires technical expertise for AI development Highly relevant to Australian practice as laparoscopic appendectomy is standard care. Could inform development of AI tools for Royal Australasian College of Surgeons training programs and support TGA approval pathways for surgical AI devices Surgeons performing laparoscopic appendectomy, AI researchers developing surgical support tools

Abstract

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 data 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. 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, preoperative laboratory parameters, and histopathologic findings, as well as standardized expert annotations of the laparoscopic grade of appendicitis). This dataset enables novel validation tasks for computer vision in laparoscopic surgery and facilitates simulation of decentralized learning approaches, overall enhancing the breadth and translational relevance of AI-based surgical video analysis.

References

1. 1.Kolbinger, F. R., Kirchner, M., Pfeiffer, K., Bodenstedt, S., Jenke, A. C., Barthel, J., Carstens, M., Dehlke, K., Dietz, S., Emmanouilidis, S., Fitze, G., Freitag, M., Holderried, F., Jacobi, T., Kanjo, W., Leitermann, L., Mees, S. T., Pistorius, S., Prudlo, C., Seiberth, A., Schultz, J., Thiel, K., Ziehn, D., Speidel, S., Weitz, J., Kather, J. N., Distler, M., & Saldanha, O. L. (2026). Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions. Scientific Data. https://doi.org/10.1097/00000658-199503000-00010