Back to Search
Start Over
Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features.
- Source :
-
IScience [iScience] 2024 Sep 27; Vol. 27 (10), pp. 111058. Date of Electronic Publication: 2024 Sep 27 (Print Publication: 2024). - Publication Year :
- 2024
-
Abstract
- Early prediction of acute pancreatitis (AP) with acute kidney injury (AKI) using abdominal contrast-enhanced CT could effectively reduce the mortality and the economic burden on patients and society. However, this challenge is limited by the imaging manifestations of early-stage AP that are not clearly visible to the naked eye. To address this, we developed a machine learning model using imperceptible variations in the structural changes of pancreas and peripancreatic region, extracted by radiomics and artificial intelligence technology, to screen and stratify the high-risk AP patients at the early stage of AP. The results demonstrate that the machine learning model could screen the high-risk AP with AKI patients with an area under the curve (AUC) of 0.82 for the external cohort, superior to the human radiologists. This finding confirms the significant potential of machine learning in the screening of acute pancreatitis and contributes to personalized treatment and management for AP patients.<br />Competing Interests: The authors declare no competing interests.<br /> (© 2024 The Author(s).)
Details
- Language :
- English
- ISSN :
- 2589-0042
- Volume :
- 27
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- IScience
- Publication Type :
- Academic Journal
- Accession number :
- 39435145
- Full Text :
- https://doi.org/10.1016/j.isci.2024.111058