Published February 23, 2023 | Version 1.03
Dataset Open

Regensburg Pediatric Appendicitis Dataset

  • 1. ETH Zurich
  • 2. Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO)
  • 3. University of Regensburg
  • 4. MIT

Description

This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict various regions of interest, such as the abdomen’s right lower quadrant, appendix, intestines, lymph nodes and reproductive organs. Alongside multiple US images for each subject, the dataset includes information encompassing laboratory tests, physical examination results, clinical scores, such as Alvarado and pediatric appendicitis scores, and expert-produced ultrasonographic findings. Lastly, the subjects were labeled w.r.t. three target variables: diagnosis (appendicitis vs. no appendicitis), management (surgical vs. conservative) and severity (complicated vs. uncomplicated or no appendicitis). The study was approved by the Ethics Committee of the University of Regensburg (no. 18-1063-101, 18-1063_1-101 and 18-1063_2-101) and was performed following applicable guidelines and regulations.

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Additional details

References

  • Associated preprint: arXiv:2302.14460
  • Marcinkevičs, R., Reis Wolfertstetter, P., Klimiene, U., Chin-Cheong, K., Paschke, A., Zerres, J., Denzinger, M., Niederberger, D., Wellmann, S., Ozkan, E., Knorr, C., & Vogt, J. E. (2024). Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. In Medical Image Analysis (Vol. 91, p. 103042). Elsevier BV. https://doi.org/10.1016/j.media.2023.103042