单位：[1]Ultrasound Medical Department, China Japan Friendship Hospital, Beijing, 100029, P.R. China[2]School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, P.R. China[3]School of Automation, Harbin University of Science and Technology, Harbin, 150080, China[4]Department of Nephrology, Molecular Cell Laboratory for Kidney Disease, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, Shanghai, P.R. China
Background: The incidence rate of renal disease is high, which can cause end-stage renal disease. Ultrasound is a commonly used imaging method, including conventional ultrasound, color ultrasound, elastography, etc. Machine learning is a potential method which has been widely used in clinical practices. Objective: To compare the diagnostic performance of different ultrasonic image measurement parameters for kidney diseases, and to compare different machine learning methods with the human-reading method. Methods: Ninety-four patients with pathologically diagnosed renal diseases and 109 normal controls were included in this study. The patients were examined by conventional ultrasound, color ultrasound and shear wave elasticity, respectively. Ultrasonic data were analyzed by Support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (AN-N), respectively, and compared with the human-reading method. Results: Only ultrasound elastography data have a diagnostic value for renal diseases. The accuracy of SVM, RF, KNN and ANN methods is 80.98%, 80.32%, 78.03% and 79.67%, respectively, while the accuracy of human-reading is 78.33%. In the data of machine learning ultrasound elastography, the elastic hardness parameters of the renal cortex are most important. Conclusion: Ultrasound elastography is of the highest diagnostic value in machine learning for nephropathy, the diagnostic efficiency of the machine learning method is slightly higher than that of the human-reading method, and the diagnostic ability of the SVM method is higher than other methods.
基金：
Science & Technology Cooperation of Nephrology [2017YFE0110500]; National Key R&D Program of China [2018YFC0114800]; Shandong Province Natural Science FoundationNatural Science Foundation of Shandong Province [ZR2018MF026]; Shandong Province Key RD Program [2019GGX101054]
第一作者单位：[1]Ultrasound Medical Department, China Japan Friendship Hospital, Beijing, 100029, P.R. China
通讯作者：
通讯机构：[1]Ultrasound Medical Department, China Japan Friendship Hospital, Beijing, 100029, P.R. China[*1]Ultrasound Medical Department, China Japan Friendship Hospital, Beijing, 100029, P.R. China
推荐引用方式(GB/T 7714)：
Li Guanghan,Liu Jian,Wu Jingping,et al.Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images[J].CURRENT MEDICAL IMAGING.2021,17(3):425-432.doi:10.2174/1573405616999200918150259.
APA：
Li Guanghan,Liu Jian,Wu Jingping,Tian Yan,Ma Liyong...&Zheng Min.(2021).Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images.CURRENT MEDICAL IMAGING,17,(3)
MLA：
Li Guanghan,et al."Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images".CURRENT MEDICAL IMAGING 17..3(2021):425-432
