Department of Ultrasound Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Beijing Research Institute, Shenzhen Mindray Biomedical Electronics Co, Ltd, Beijing, China.
J Ultrasound Med. 2020 Jan;39(1):51-59. doi: 10.1002/jum.15070. Epub 2019 Jun 20.
To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques.
A total of 240 participants were recruited and divided into 4 groups (normal, mild, moderate, and severe NAFLD groups), according to the definition and the ultrasound scoring system for NAFLD. Two-dimensional hepatic imaging was analyzed by the envelope signal, grayscale signal, and deep-learning index obtained by 3 image-processing techniques. The values of the 3 methods ranged from 0 to 65,535, 0 to 255, and 0 to 4, respectively. We compared the values among the 4 groups, draw receiver operating characteristic curves, and compared the area under the curve (AUC) values to identify the best image-processing technique.
The envelope signal value, grayscale value, and deep-learning index had a significant difference between groups and increased with the severity of NAFLD (P < .05). The 3 methods showed good ability (AUC > 0.7) to identify NAFLD. Meanwhile, the deep-learning index showed the superior diagnostic ability in distinguishing moderate and severe NAFLD (AUC = 0.958).
The envelope signal and grayscale values were vital parameters in the diagnosis of NAFLD. Furthermore, deep learning had the best sensitivity and specificity in assessing the severity of NAFLD.
通过比较 3 种图像处理技术，验证深度学习在诊断非酒精性脂肪性肝病（NAFLD）中的价值。
共招募 240 名参与者，根据 NAFLD 的定义和超声评分系统将其分为 4 组（正常、轻度、中度和重度 NAFLD 组）。用 3 种图像处理技术得到的包络信号、灰度信号和深度学习指数对二维肝脏图像进行分析。3 种方法的值范围分别为 0 至 65535、0 至 255 和 0 至 4。比较 4 组间的值，绘制受试者工作特征曲线，并比较曲线下面积（AUC）值，以确定最佳图像处理技术。
各组间的包络信号值、灰度值和深度学习指数均有显著差异，且随 NAFLD 严重程度增加而增加（P < .05）。3 种方法均具有良好的识别 NAFLD 的能力（AUC> 0.7）。同时，深度学习指数在鉴别中、重度 NAFLD 方面具有较好的诊断能力（AUC=0.958）。
包络信号和灰度值是诊断 NAFLD 的重要参数。此外，深度学习在评估 NAFLD 严重程度方面具有最佳的敏感性和特异性。
