Browsing by Author "Kanjo, Weam"

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Open Access
Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions
(Technische Universität Dresden, 2026-04-29) Kolbinger, Fiona R; Kirchner, Max; Pfeiffer, Kevin; Bodenstedt, Sebastian; Jenke, Alexander C; Barthel, Julia; Carstens, Matthias; Dehlke, Karolin; Dietz, Sophia; Emmanouilidis, Sotirios; Fitze, Guido; Freitag, Martin; Holderried, Fabian; Jacobi, Thorsten; Kanjo, Weam; Leitermann, Linda; Mees, Sören Torge; Pistorius, Steffen; Prudlo, Conrad; Seiberth, Astrid; Schultz, Jurek; Thiel, Karolin; Ziehn, Daniel; Speidel, Stefanie; Kather, Jakob Nikolas; Distler, Marius; Saldanha, Oliver Lester
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 histopathological 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.
Open Access
FedSurg EndoVis 2024: Challenge Subset of Appendix300
(Technische Universität Dresden, 2026-08-27) Kirchner, Max; Kolbinger, Fiona R; Jenke, Alexander C; Saldanha, Oliver L; Pfeiffer, Kevin; Kanjo, Weam; Kather, Jakob N.; Bodenstedt, Sebastian; Speidel, Stefanie
This deposit contains the supplementary data for the FedSurg EndoVis 2024 Challenge, the first federated learning challenge in Surgical Data Science, held at MICCAI 2024. The challenge used a preliminary subset of the Appendix300 dataset, a multi-institutional collection of laparoscopic appendectomy recordings from German university and community hospitals, annotated with intraoperative appendicitis severity grades 0 to 5 following Gomes et al. The challenge cohort comprises 223 recordings across four centers, split into 153 training and 70 test cases. The deposit includes a CSV file specifying which Appendix300 samples were used in the challenge and their assignment to centers and to the training and test splits, together with two recordings that were used in the challenge but excluded from the final Appendix300 dataset because the available footage was shorter than the nominal 100 second window or the appendix was not clearly visible at the annotated timestamp. This deposit does not contain the appendectomy video recordings themselves, with the exception of the two excluded cases named above. The video data and accompanying clinical metadata are available separately as the Appendix300 dataset at https://doi.org/10.25532/OPARA-1173. The challenge evaluation and ranking code is available at https://gitlab.com/nct_tso_public/challenges/miccai2024/snippet, and the example federated learning setup at https://gitlab.com/nct_tso_public/challenges/miccai2024/FedSurg24. Use of this material requires citation of both the FedSurg challenge paper and the Appendix300 data descriptor. Released under CC BY.