(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. PDF  XML

中文翻译：

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基于AI的算法的概念：对CEUS图像和HSP进行分析以识别严重急性胰腺炎的早期实质性变化

（1）背景：识别早期胰腺实质改变仍然是一项具有挑战性的放射学诊断任务。在这项研究中，我们假设将人工智能（AI）应用于对比增强超声（CEUS）并测量热休克蛋白（HSP）-70水平可以改善急性胰腺炎中早期胰腺坏死的检测。（2）方法：急性胰腺炎$（n = 146）$和年龄和性别相匹配的健康对照组$（n = 50）$被纳入研究。急性胰腺炎的严重程度根据修订后的亚特兰大分类确定。对所选重症急性胰腺炎（AP）患者和年龄/性别匹配的健康对照进行了算法初始化分析。入院时收集胰腺炎患者的外周血样品，并使用ELISA测量HSP-70水平。CEUS设备获取了多个机械指数对比度特定模式图像。手动选择感兴趣的二维（2D）空间区域（ROI）的轮廓，然后计算一组定量参数。使用MATLAB软件中实现的算法进行图像处理计算和从CEUS诊断图像中提取定量参数。（3）结果：血清HSP-70水平为100.246 ng / ml（平均76。4 ng / ml）在诊断急性胰腺炎时。与受坏死影响的实质中的CEUS Peek值相比，健康胰腺的CEUS Peek值更高（155.5），平均通过时间更长（40.1 s）。（4）结论：提取的定量参数和HSP-70生化变化适合进一步用于基于AI的胰腺病理病例分类和自动评估胰腺坏死。PDF XML 提取的定量参数和HSP-70生化变化适合进一步用于基于AI的胰腺病理病例分类和自动评估AP中胰腺坏死。PDF XML 提取的定量参数和HSP-70生化变化适合进一步用于基于AI的胰腺病理病例分类和自动评估AP中胰腺坏死。PDF XML