OBJECTIVES The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultrasonography images. METHODS Datasets from two tertiary academic referral centers were used. A total of 13,608 ultrasonography images from 3446 patients who underwent surgical resection, biopsy, or transient elastography were used for training a DCNN for the prediction of the METAVIR score. Pathological specimens or estimated METAVIR scores derived from transient elastography were used as a reference standard. A four-class model (F0 vs. F1 vs. F23 vs. F4) was developed. Diagnostic performance of the algorithm was validated on a separate internal test set of 266 patients with 300 images and external test set of 572 patients with 1232 images. Performance in classification of cirrhosis was compared between the DCNN and five radiologists. RESULTS The accuracy of the four-class model was 83.5% and 76.4% on the internal and external test set, respectively. The area under the receiver operating characteristic curve (AUC) for classification of cirrhosis (F4) was 0.901 (95% confidence interval [CI], 0.865-0.937) on the internal test set and 0.857 (95% CI, 0.825-0.889) on the external test set, respectively. The AUC of the DCNN for classification of cirrhosis (0.857) was significantly higher than that of all five radiologists (AUC range, 0.656-0.816; p value < 0.05) using the external test set. CONCLUSIONS The DCNN showed high accuracy for determining METAVIR score using ultrasonography images and achieved better performance than that of radiologists in the diagnosis of cirrhosis. KEY POINTS • DCNN accurately classified the ultrasonography images according to the METAVIR score. • The AUROC of this algorithm for cirrhosis assessment was significantly higher than that of radiologists. • DCNN using US images may offer an alternative tool for monitoring liver fibrosis.

中文翻译：

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超声检查深度学习：使用深度卷积神经网络对肝纤维化进行自动分类。

目的本研究的目的是开发一个深层卷积神经网络（DCNN），用于使用B型超声图像预测METAVIR评分。方法使用来自两个大专院校转诊中心的数据集。来自3446例接受手术切除，活检或短暂弹性成像的患者的13608张超声图像用于训练DCNN以预测METAVIR评分。从瞬时弹性成像得出的病理标本或估计的METAVIR分数用作参考标准。开发了四类模型（F0对F1对F23对F4）。该算法的诊断性能在266个具有300张图像的内部测试集和572个具有1232图像的外部测试集上得到了验证。在DCNN和五位放射科医生之间比较了肝硬化分类的表现。结果在内部和外部测试集上，四级模型的准确性分别为83.5％和76.4％。在内部测试装置上，用于分类为肝硬化（F4）的接收器工作特性曲线（AUC）下的面积为0.901（95％置信区间[CI]，0.865-0.937），而在内部测试仪上为0.857（95％CI，0.825-0.889）外部测试集。使用外部测试集，用于肝硬化分类的DCNN的AUC（0.857）明显高于所有五位放射科医生的AUC（AUC范围，0.656-0.816； p值<0.05）。结论DCNN在使用超声图像确定METAVIR评分方面显示出很高的准确性，并且在诊断肝硬化方面比放射科医师表现更好。要点•DCNN根据METAVIR评分将超声图像准确分类。•用于肝硬化评估的该算法的AUROC显着高于放射科医生。•使用US图像的DCNN可能提供监视肝脏纤维化的替代工具。