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

I-Min Chiu1,2, Teng-Yi Huang3, David Ouyang4

  • 1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. outofray@hotmail.com.

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A new deep learning model accurately detects pneumoperitoneum in computed tomography (CT) scans, improving diagnostic speed and patient outcomes. This AI tool shows high sensitivity and specificity, aiding emergency care decisions.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Diagnostic Imaging

Background:

  • Delayed or missed detection of pneumoperitoneum (free air in the abdominal cavity) is associated with increased patient mortality and morbidity.
  • Accurate and timely identification of pneumoperitoneum is critical for effective emergency care and surgical intervention.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for the automated detection of pneumoperitoneum in computed tomography (CT) images.
  • To assess the model's performance across retrospective, prospective, and external validation datasets.

Main Methods:

  • A deep learning model was trained on abdominal CT scans from Far Eastern Memorial Hospital (2012-2021).
  • Model performance was evaluated on a simulated test set (14,039 scans) and a prospective test set (6,351 scans) from the same institution.
  • External validation was performed using 480 scans from Cedars-Sinai Medical Center.

Main Results:

  • The DL model demonstrated high diagnostic accuracy, achieving a sensitivity of 0.81-0.83 and a specificity of 0.97-0.99 across all validation sets.
  • Sensitivity improved to 0.92-0.98 when excluding cases with minimal free air (total volume <10 mL).
  • The model provided consistent predictions for pneumoperitoneum detection in CT scans.

Conclusions:

  • The developed deep learning model offers accurate and reliable identification of pneumoperitoneum on CT scans.
  • This AI tool has the potential to expedite diagnostic and treatment workflows in emergency settings.
  • The model's performance suggests its utility in improving patient care by reducing diagnostic delays.