Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images

Title:Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images

Volume: 17

Author(s): Guanghan Li, Jian Liu, Jingping Wu, Yan Tian, Liyong Ma, Yuejun Liu, Bo Zhang, Shan Mou and Min Zheng*

Affiliation:

• Ultrasound Medical Department, China Japan Friendship Hospital, Beijing, 100029,China

Keywords: Renal disease, ultrasound image, diagnosis, machine learning, elastography, support vector machine.

Abstract:

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 (ANN), 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.

Cite this article as:

Li Guanghan , Liu Jian , Wu Jingping , Tian Yan , Ma Liyong , Liu Yuejun , Zhang Bo , Mou Shan and Zheng Min *, Diagnosis of Renal Diseases Based on Machine Learning Methods Using Ultrasound Images, Current Medical Imaging 2021; 17 (3) . https://dx.doi.org/10.2174/1573405616999200918150259

DOI https://dx.doi.org/10.2174/1573405616999200918150259	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603