PDF(Pubmed)
Abstract：
BACKGROUND: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis.
METHODS: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People\'s Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model\'s performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU).
RESULTS: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis.
CONCLUSIONS: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition.
BACKGROUND: This is a retrospective study.
METHODS: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People\'s Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model\'s performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU).
RESULTS: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis.
CONCLUSIONS: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition.
BACKGROUND: This is a retrospective study.
摘要：
背景：急性胰腺炎是需要急诊手术的最常见疾病之一。快速准确地识别急性胰腺炎有助于改善临床预后。本研究旨在开发一种基于深度学习的急性胰腺炎诊断模型。
方法：在这项调查中，我们纳入了2020年1月至2021年12月四川省人民医院收治的190例急性胰腺炎患者的队列.从急性胰腺炎患者和健康个体获得腹部计算机断层扫描（CT）扫描。我们的模型使用两个模块构建：（1）急性胰腺炎分类器模块；（2）胰腺炎病变分割模块。每个模型的性能都是根据精度进行评估的，召回率，F1分数，曲线下面积(AUC),损失率，频率加权精度(fwavacc)，和平均交汇处(MIOU)。
结果：入院时，在轻度和重度急性胰腺炎患者之间观察到炎症指标的显着差异，肝脏,和肾功能指标，以及凝血参数。急性胰腺炎分类器模块表现出良好的诊断效能，在测试集中显示令人印象深刻的AUC为0.993（95CI：0.978-0.999）（包括健康检查患者与那些患有急性胰腺炎的人，P<0.001），外部验证集的AUC为0.850（95CI：0.790-0.898）（健康检查患者与急性胰腺炎患者，P<0.001)。此外，急性胰腺炎病变分割模块在验证集中表现突出.对于胰腺分割，胰周炎症渗出，胰周积液,胰周脓肿坏死，MIOU值为86.02（84.52，87.20），61.81(56.25,64.83),57.73(49.90,68.23),和66.36（55.08，72.12），分别。这些发现强调了所开发模型在准确表征和评估急性胰腺炎方面的稳健性和可靠性。
结论：急性胰腺炎的诊断模型，由深度学习驱动，在准确评估病情的严重程度方面表现出优异的功效。
背景：这是一项回顾性研究。
方法：在这项调查中，我们纳入了2020年1月至2021年12月四川省人民医院收治的190例急性胰腺炎患者的队列.从急性胰腺炎患者和健康个体获得腹部计算机断层扫描（CT）扫描。我们的模型使用两个模块构建：（1）急性胰腺炎分类器模块；（2）胰腺炎病变分割模块。每个模型的性能都是根据精度进行评估的，召回率，F1分数，曲线下面积(AUC),损失率，频率加权精度(fwavacc)，和平均交汇处(MIOU)。
结果：入院时，在轻度和重度急性胰腺炎患者之间观察到炎症指标的显着差异，肝脏,和肾功能指标，以及凝血参数。急性胰腺炎分类器模块表现出良好的诊断效能，在测试集中显示令人印象深刻的AUC为0.993（95CI：0.978-0.999）（包括健康检查患者与那些患有急性胰腺炎的人，P<0.001），外部验证集的AUC为0.850（95CI：0.790-0.898）（健康检查患者与急性胰腺炎患者，P<0.001)。此外，急性胰腺炎病变分割模块在验证集中表现突出.对于胰腺分割，胰周炎症渗出，胰周积液,胰周脓肿坏死，MIOU值为86.02（84.52，87.20），61.81(56.25,64.83),57.73(49.90,68.23),和66.36（55.08，72.12），分别。这些发现强调了所开发模型在准确表征和评估急性胰腺炎方面的稳健性和可靠性。
结论：急性胰腺炎的诊断模型，由深度学习驱动，在准确评估病情的严重程度方面表现出优异的功效。
背景：这是一项回顾性研究。
