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

• Published in: Nature communications Vol. 15; no. 1; pp. 9660 - 7
• Main Authors: Chiu, I-Min, Huang, Teng-Yi, Ouyang, David, Lin, Wei-Che, Pan, Yi-Ju, Lu, Chia-Yin, Kuo, Kuei-Hong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 59; 631/114/1305; 692/700/1421/2025; Abdomen; Abdomen - diagnostic imaging; Adult; Aged; Algorithms; Computed tomography; Deep Learning; Emergency medical care; Emergency medical services; Female; Health care facilities; Health services; Humanities and Social Sciences; Humans; Machine learning; Male; Medical imaging; Middle Aged; Morbidity; multidisciplinary; Pneumoperitoneum - diagnostic imaging; Prospective Studies; Radiography, Abdominal - methods; Retrospective Studies; Science; Science (multidisciplinary); Sensitivity and Specificity; Test sets; Tomography; Tomography, X-Ray Computed - methods
• DOI: 10.1038/s41467-024-54043-1
• ISSN: 2041-1723

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 <10 ml) are excluded. These findings suggest that the model can deliver accurate and consistent predictions for pneumoperitoneum in computed tomography scans with segmented masks, potentially accelerating diagnostic and treatment workflows in emergency care. CT scans are routinely used to diagnose pneumoperitoneum. Here the authors present a deep learning model for detecting pneumoperitoneum in abdominal CT scans, validated on datasets from Taiwan and the US, showing high accuracy and potential to accelerate diagnosis and treatment in emergency care.