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Related Experiment Video

Updated: Jul 10, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Anatomically Localized Detection of Six Acute Abdominal Emergencies on CT Using Multi-window Deep Learning:

Hasan Mete Erdoğan1, Ural Koç2

  • 1Budapest University of Technology and Economics, Budapest, Hungary. hasanmete.erdogan@edu.bme.hu.

Journal of Imaging Informatics in Medicine
|July 8, 2026
PubMed
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This study developed a deep learning system for detecting six acute abdominal emergencies on CT scans. The model showed high accuracy internally and moderate-to-high performance externally, but requires further validation before clinical use.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Acute abdominal emergencies require timely diagnosis for effective treatment.
  • Deep learning models offer potential for automating the detection of these conditions on CT scans.

Purpose of the Study:

  • To develop and validate a deep learning system for classifying and localizing six acute abdominal emergencies using CT.
  • To assess the system's performance using multi-window Hounsfield Unit (HU) encoding and anatomical localization.

Main Methods:

  • A YOLOv11-Large model was trained on a large national teleradiology dataset (1274 patients) with multi-window HU encoding.
  • Localization was evaluated using a nine-region abdominal grid, and specificity was tested in a target-negative cohort.
  • External validation was performed on the Stanford Merlin cohort (280 patients).
Keywords:
Abdominal regionsAcute abdomenComputed tomographyDeep learningEmergency radiologyMulti-window encoding

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Main Results:

  • Internal testing achieved a macro AUROC of 0.941 and macro F1 of 76.1%.
  • Nine-region localization accuracy was high (99.5% for detected cases).
  • External validation on the Stanford Merlin cohort showed a macro AUROC of 0.879, with moderate-to-high discrimination for all six classes.

Conclusions:

  • The developed deep learning model demonstrates strong performance in detecting acute abdominal emergencies on CT.
  • While promising, further prospective, multi-site validation and site-specific calibration are necessary before clinical implementation.
  • Anatomical localization using the nine-region grid provides clinically relevant interpretability.