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PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans

Author

Listed:
  • I-Min Chiu

    (Cedars-Sinai Medical Center
    Kaohsiung Chang Gung Memorial Hospital)

  • Teng-Yi Huang

    (National Taiwan University of Science and Technology)

  • David Ouyang

    (Cedars-Sinai Medical Center)

  • Wei-Che Lin

    (Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine
    Kaohsiung Chang Gung Memorial Hospital
    National Sun Yat-Sen University)

  • Yi-Ju Pan

    (Far Eastern Memorial Hospital
    Yuan Ze University)

  • Chia-Yin Lu

    (Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine)

  • Kuei-Hong Kuo

    (Far Eastern Memorial Hospital
    National Yang Ming Chiao Tung University School of Medicine)

Abstract

Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abdominal scans from Far Eastern Memorial Hospital (January 2012–December 2021) and evaluated using a simulated test set (14,039 scans) and a prospective test set (6351 scans) collected from the same center between December 2022 and May 2023. External validation included 480 scans from Cedars-Sinai Medical Center. Overall, the model achieves a sensitivity of 0.81–0.83 and a specificity of 0.97–0.99 across retrospective, prospective, and external validation; sensitivity improves to 0.92–0.98 when cases with a small amount of free air (total volume

Suggested Citation

  • I-Min Chiu & Teng-Yi Huang & David Ouyang & Wei-Che Lin & Yi-Ju Pan & Chia-Yin Lu & Kuei-Hong Kuo, 2024. "PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54043-1
    DOI: 10.1038/s41467-024-54043-1
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