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

I-Min Chiu (), Teng-Yi Huang, David Ouyang, Wei-Che Lin, Yi-Ju Pan, Chia-Yin Lu and Kuei-Hong Kuo ()
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I-Min Chiu: Cedars-Sinai Medical Center
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
Yi-Ju Pan: Far Eastern Memorial Hospital
Chia-Yin Lu: Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine
Kuei-Hong Kuo: Far Eastern Memorial Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-7

Abstract: 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

Date: 2024
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DOI: 10.1038/s41467-024-54043-1

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