ArticleAnnals of medicine2024
Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05498961 (The Effect of Gallstone Characteristics for the Severity of Acute Gallstone Pancreatitis), which is not on this map. Cited by 9 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Effect of Gallstone Characteristics for the Severity of Acute Gallstone Pancreatitis
Who cites it
9 citing papers in PubMed.
- Explainable early prediction of short-term severe deterioration in gallstone-related biliary disease using MIMIC-IV, eICU, and local real-world data.BMC gastroenterology · 2026Article
- Radiomics analysis based on MRCP for predicting post-ERCP pancreatitis in patients with common bile duct stones.Surgical endoscopy · 2026Article
- Development of a prediction method for severe pancreatitis using a nomogram.Frontiers in medicine · 2026Article
- Management of acute pyelonephritis in pregnancy in emergency settings: A comparative study of conservative therapy and renal drainage.Journal of education and health promotion · 2026Article
- Predicting the acute pancreatitis severity with multi-machine learning models: constructing an online prediction platform.Frontiers in cellular and infection microbiology · 2026Article
- Development and validation of gallstone disease risk factor identification model: a cross-sectional study in Western China.Frontiers in nutrition · 2026Article
- Rational evaluation of computed tomography scans using a risk-adjusted model in a comprehensive public tertiary hospital in China.Frontiers in health services · 2026Article
- Computed tomography-based conventional imaging features and texture analysis characteristics of chemotherapy drug-related acute pancreatitis.Frontiers in medicine · 2025Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity.
methodsWe retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961).
resultsA total of 301 patients were enrolled. They were randomly assigned into the training (
conclusionsWe built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.