Gastroenterology Department, University of Medicine and Pharmacy, Craiova, Romania.
Clin Gastroenterol Hepatol. 2012 Jan;10(1):84-90.e1. doi: 10.1016/j.cgh.2011.09.014. Epub 2011 Oct 1.
BACKGROUND & AIMS: By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis.
We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns.
The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85.
Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses.
实时内镜超声（EUS）弹性成像是通过应变评估来提供胰腺病变特征的附加信息。我们通过人工神经网络分析的计算机辅助诊断评估了实时 EUS 弹性成像在局灶性胰腺病变中的准确性。
我们在欧洲 13 家三级学术医疗中心进行了一项前瞻性、盲法、多中心研究，共纳入 258 例患者（EUS 弹性成像 774 次记录），其中慢性胰腺炎 47 例，胰腺腺癌 211 例（欧洲 EUS 弹性成像多中心研究组）。我们使用后处理软件分析，通过从 EUS 弹性成像的动态序列中检索色调直方图数据，将弹性成像电影的单个帧计算到数字矩阵中。然后，我们通过扩展神经网络分析来分析数据，以自动区分良性和恶性模式。
神经计算方法的训练准确率为 91.14%（95%置信区间 [CI]，89.87%-92.42%），测试准确率为 84.27%（95%CI，83.09%-85.44%）。这些结果是通过 10 折交叉验证技术获得的。分类过程的统计分析显示，敏感性为 87.59%，特异性为 82.94%，阳性预测值为 96.25%，阴性预测值为 57.22%。此外，相应的接收器工作特征曲线下面积为 0.94（95%CI，0.91%-0.97%），明显高于简单平均色调直方图分析的相应值，后者的接收器工作特征曲线下面积为 0.85。
通过人工神经网络的人工智能方法的使用支持了医疗决策过程，提供了快速准确的诊断。
