The Concept of AI-Based Algorithm: Analysis of CEUS Images and HSPs for Identification of Early Parenchymal Changes in Severe Acute Pancreatitis

Aiste Kielaite-Gulla,Gintautas Dzemyda,Arturas Samuilis,Renaldas Raisutis,Kestutis Strupas

doi:10.15388/21-infor453

Abstract

(1) Background: Identifying early pancreas parenchymal changes remains a challenging radiologic diagnostic task. In this study, we hypothesized that applying artificial intelligence (AI) to contrast-enhanced ultrasound (CEUS) along with measurement of Heat Shock Protein (HSP)-70 levels could improve detection of early pancreatic necrosis in acute pancreatitis. (2) Methods: Acute pancreatitis $(n=146)$ and age- and sex matched healthy controls $(n=50)$ were enrolled in the study. The severity of acute pancreatitis was determined according to the revised Atlanta classification. The selected severe acute pancreatitis (AP) patient and an age/sex-matched healthy control were analysed for the algorithm initiation. Peripheral blood samples from the pancreatitis patient were collected on admission and HSP-70 levels were measured using ELISA. A CEUS device acquired multiple mechanical index contrast-specific mode images. Manual contour selection of the two-dimensional (2D) spatial region of interest (ROI) followed by calculations of the set of quantitative parameters. Image processing calculations and extraction of quantitative parameters from the CEUS diagnostic images were performed using algorithms implemented in the MATLAB software. (3) Results: Serum HSP-70 levels were 100.246 ng/ml (mean 76.4 ng/ml) at the time of the acute pancreatitis diagnosis. The CEUS Peek value was higher (155.5) and the mean transit time was longer (40.1 s) for healthy pancreas than in parenchyma affected by necrosis (46.5 and 34.6 s, respectively). (4) Conclusions: The extracted quantitative parameters and HSP-70 biochemical changes are suitable to be used further for AI-based classification of pancreas pathology cases and automatic estimation of pancreatic necrosis in AP.

Highlights

Acute pancreatitis (AP) is one of the most common gastrointestinal disorders, and cases have been steadily increasing over the past few decades (Yadav and Whitcomb, 2010)
We hypothesized that applying artificial intelligence (AI) to contrast-enhanced ultrasound (CEUS) along with measurement of Heat Shock Protein (HSP)70 levels could improve detection of early pancreatic necrosis in acute pancreatitis
The patient’s condition became unstable, and he was transferred to the Intensive Care Unit (ICU), where a computed tomography (CT) scan (Fig. 2) and CEUS were simultaneously performed

Summary

Introduction

Acute pancreatitis (AP) is one of the most common gastrointestinal disorders, and cases have been steadily increasing over the past few decades (Yadav and Whitcomb, 2010). The severe form of the disease is accompanied by serious complications and is reported to have a high mortality rate (Frossard et al, 2008). Imaging is recommended to confirm the clinical diagnosis, evaluate the cause of the abdominal pain, and grade the extent and severity of acute pancreatitis (Fei and Li, 2017). Heat shock proteins (HSPs), HSP70, have received increasing attention as a stress-responsive protein. It plays a role in the pathogenesis and development of acute pancreatitis (Frossard et al, 2008). The increased expression of HSP-70 associated with pancreatic inflammation may confer a protective effect for the remaining acini after acute pancreatitis (Feng and Li, 2010)

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