Artificial neural networks accurately predict intra‐abdominal infection in moderately severe and severe acute pancreatitis

Abstract

Objective: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression model (LRM). Methods: Patients suffering from MSAP or SAP from July 2014 to June 2017 in three affiliated hospitals of the Army Medical University in Chongqing, China, were enrolled in this study. A univariate analysis was used to determine the different parameters between patients with and without intra-abdominal infection. Subsequently, these parameters were used to build LRM and ANN. Results: Altogether 263 patients with MSAP or SAP were enrolled in this retrospective study. A total of 16 parameters that differed between patients with and without intra-abdominal infection were used to construct both models. The sensitivity of ANN and LRM was 80.99% (95% confidence interval [CI] 72.63-87.33) and 70.25% (95% CI 61.15-78.04), respectively (P > 0.05), whereas the specificity was 89.44% (95% CI 82.89-93.77) and 77.46% (95% CI 69.54-83.87), respectively (P < 0.05). ANN predicted the risk of intra-abdominal infection better than LRM (area under the receiver operating characteristic curve: 0.923 [0.883-0.952] vs 0.802 [0.749-0.849], P < 0.001). Conclusions: ANN accurately predicted intra-abdominal infection in MSAP and SAPand is an ideal tool for predicting intra-abdominal infection in such patients. Coagulation parameters played an important role in such prediction.intra-abdominal infection, logistic regression, neural network, pancreatitis Male sex, n (%) 165 (62.7) Age, y (median [IQR]) 47 (39-59) History of smoking, n (%) 76 (28.9) History of alcohol consumption, n (%) 85 (32.3) History of hypertension, n (%) 58 (22.1) History of diabetes, n (%) 31 (11.8) Admission to different departments, n (%) Department of Gastroenterology 127 (48.3) Department of Emergency 67 (25.5) Intensive care unit 69 (26.2) Etiology, n (%) Biliary 106 (40.3) Hypertriglyceridemia 92 (35.0) Alcoholic 24 (9.1) Others 41 (15.6) BMI, kg/m 2 (median [IQR]) 25.71 (23.53-27.92) Obese (BMI ≥25 kg/m 2 ), n (%) 146 (55.5) SIRS, n (%) 200 (76.0) Intra-abdominal infection, n (%) 121 (46.0) APACHE II score, median (IQR) 10 (8-13) Abbreviations: APACHE, acute physiology and chronic health evaluation; BMI, body mass index; IQR, interquartile range; SIRS, systemic inflammatory response syndrome.

Artificial intelligence in pancreaticobiliary endoscopy

“…Several other studies using AI‐based methods reported similar results 9,11–15 . AI‐based methods could also identify specific complications of AP such as porto‐venous thrombosis with an accuracy of 83% 16 and intra‐abdominal infections with a receiver operating characteristic curve of 0.923 [0.883–0.952] 17 …”

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

“…As a result, several potentially important factors may have been excluded prior to the ANN modeling. Therefore, due to the complex learning processes involved in the model development, it may be impossible to figure out what parameters exist in the hidden layers (the so-called black box) or to reproduce the same ANN model ( 12 , 18 ). Although the dropout method was applied to avoid bias and overfitting, and our network was validated for stability, there is risk when applying this network to a larger data set from multiple institutions ( 25 ).…”

“…The artificial neural network (ANN) is a non-linear regression model and can be used in a computer-aided diagnosis system ( 12 ). An ANN model based on a combination of non-invasive clinical parameters from BA patients and their differential diagnosis was developed.…”

Artificial intelligence for the management of pancreatic diseases

“…Three studies aimed to predict complications by using an ANN and compared it to logistic regression (LR) modeling. The results showed that the ANN significantly outperformed the LR modeling in predicting the occurrence of several complications during the course of the disease in all three studies 17–19 . Two studies reported ANNs that predict multi‐organ failure (MOF) in AP patients based on clinical and laboratory findings.…”

“…Several studies report ANNs that predict complications and mortality in patients with AP with high accuracy, ranging from 83.0% to 97.5% 17–23 . Three studies aimed to predict complications by using an ANN and compared it to logistic regression (LR) modeling.…”