ArticleiScience2024
Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer.Frontiers in medicine · 2026Article
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Authors and funding
9 authors.
Funding
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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.
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Registered trials
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