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

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

SUBMITTER: Chiu IM 

PROVIDER: S-EPMC11544264 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature