Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology

Figure 1 Flow chart for development and external independent validation of microlithiasis prediction score. In the Ludwig-Maximilians-Universität in Munich identification cohort, 218 acute pancreatitis patients treated as inpatients between 2015-2020 were included in the final machine learning-based score survey. The validation cohort, consisting of 117 pancreatitis cases, was composed of patient data from the University Hospital of Göttingen and Technical University Munich. The microlithiasis predictive model was trained using data from both biliary sludge and biliary microlithiasis patients to cover the entirety of biliary microconcrements and to reflect the current lack of uniform definitions of biliary sludge and biliary microlithiasis in clinical practice. EUS: Endosonography; AP: Acute pancreatitis.

Figure 2 Machine-learning based model for the prediction of biliary sludge and microlithiasis in the context of acute (presumed) idiopathic acute pancreatitis. Of the initial 192 variables analysed, 154 were included in the categorisation step after excluding those variables without evidence of variable variance. Using an auto-machine learning approach, the final (iterative) predictive model was developed via the base model step. ML: Machine learning.

Figure 3 Graphical representation of the prediction model variables according to importance of scale. A: Variables of the final (iterated) auto-machine learning prediction model are ordered by scale of importance; B and C: Precoat diagram showing robust positive and negative prediction (3/81 patient cases were misclassified as microlithiasis and not other-acute pancreatitis). Gamma-GT: Gamma-glutamyl transpeptidase; AST: Aspartate aminotransferase; GOT: Glutamic oxalacetic transaminases; ALT: Alanine transaminase; GPT: Glutamic pyruvic transaminase; LDH: Lactate dehydrogenase; RDW: Red blood cell distribution width.