Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain: A clinical diagnostic test accuracy study

Brejnebøl, Mathias Willadsen; Nielsen, Yousef W.; Taubmann, Oliver; Eibenberger, Eva; Müller, F.

doi:10.1016/j.ejrad.2022.110216

Cited by 9 publications

(6 citation statements)

References 20 publications

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Section: Discussionmentioning

confidence: 99%

“…Historically, AI algorithms have encountered challenges when attempting to detect free air in CT scans. They often exhibit reduced sensitivity, even if their specificity is commendable 17,18 . Previous studies, focusing on the utilization of 2D segmentation models for pneumoperitoneum detection, have highlighted challenges in differentiating free air from the common place bowel gas 18 .…”

Section: Discussionmentioning

confidence: 99%

“…They often exhibit reduced sensitivity, even if their specificity is commendable 17,18 . Previous studies, focusing on the utilization of 2D segmentation models for pneumoperitoneum detection, have highlighted challenges in differentiating free air from the common place bowel gas 18 . While 2D models have been a cornerstone in healthcare deep learning applications, this is largely because many medical imaging modalities, such as X- rays, ultrasound, and specific MRI or CT slices, intrinsically generate 2D images, making these models a natural choice 19-22 .…”

Section: Discussionmentioning

confidence: 99%

“…AI has also contributed to increased speed and efficiency in medical image analysis, reducing the workload of healthcare professionals and improving patient outcomes. Recent studies have investigated the potential of deep learning algorithms in assisting the detection of pneumoperitoneum on CT scans 17,18 . However, the performance of these AI models varies and is dependent on the selection of datasets.…”

Section: Introductionmentioning

confidence: 99%

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PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

Chiu,

Huang,

Kuo

2024

Preprint

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Pneumoperitoneum, necessitates surgical intervention in 85-90% of cases, relies heavily on CT scans for diagnosis. Delay or misdiagnosis in detecting pneumoperitoneum can significantly increase mortality and morbidity. Our study introduced PACT-3D, a deep learning model developed to identify pneumoperitoneum in CT images. In this single hospital study, we retrospectively reviewed abdominal CT scans from January 2012 to December 2021, excluded CT of image acquisition error and without reports to form the development dataset for training the model. We evaluated the PACT- 3D model using a simulated test set of 14,039 scans and a prospective test set of 6,351 scans, collected from December 2022 to May 2023 at the same center. PACT-3D achieved a sensitivity of 0.81 and a specificity of 0.99 in retrospective testing, with prospective validation yielding a sensitivity of 0.83 and a specificity of 0.99. Sensitivity improved to 0.95 and 0.98 when excluding cases with a small amount of free air (total volume < 10ml) in simulated and prospective test sets, respectively. By delivering accurate and consistent patient-level predictions and providing segmented masks, PACT- 3D holds significant potential for assisting rapid decision-making in emergency care, thereby potentially improving patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

Chiu,

Huang,

Kuo

2024

Preprint

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“…Since then, AI has begun to play a crucial role in the healthcare system and has been consistently optimized. Brejnebøl et al ( 58 ) demonstrated that AI algorithms based on CT scans have benefited the diagnosis of patients with acute appendicitis, albeit with low sensitivity ( 58 ). In a recent review, Lam et al confirmed the significant role of AI in predicting acute appendicitis and emphasized the need for its development in terms of clinical usability ( 59 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Gan,

Liu,

Liu

et al. 2024

Front. Med.

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BackgroundAcute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.MethodsIn this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.ResultsEleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.ConclusionThe ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.

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Artificial Intelligence in Trauma Imaging

Elbanan,

Sagreiya

2023

Trauma Computed Tomography

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Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain: A clinical diagnostic test accuracy study

Cited by 9 publications

References 20 publications

PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Artificial Intelligence in Trauma Imaging

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