卷积神经网络
医学
人工智能
深度学习
接收机工作特性
人工神经网络
残余物
磁共振成像
灵敏度(控制系统)
曲线下面积
循环神经网络
模式识别(心理学)
机器学习
放射科
计算机科学
算法
内科学
工程类
药代动力学
电子工程
作者
Chen-Yi Lin,Xuefei Song,Lunhao Li,Yinwei Li,Mengda Jiang,Rou Sun,Huifang Zhou,Xianqun Fan
标识
DOI:10.1186/s12886-020-01783-5
摘要
Abstract Background This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations. Methods A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People’s Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks. Results Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021). Conclusions The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.
科研通智能强力驱动
Strongly Powered by AbleSci AI