Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning

By C. Kuo, Chun-Min Chang, Kuan-Ting Liu, Wei-Kai Lin, H. Chiang, C. Chung, Meng-Ru Ho, Pei Sun, Ronghua Yang, Kuan-Ta Chen. 2019

187 citations recorded by the source corpus.

Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model’s generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m2. A Pearson correlation coefficient of 0.741 indicated the strong relationship between artificial intelligence (AI)- and creatinine-based GFR estimations. Overall CKD status classification accuracy of our model was 85.6% —higher than that of experienced nephrologists (60.3%–80.1%). Our model is the first fundamental step toward realizing the potential of transforming kidney ultrasound imaging into an effective, real-time, distant screening tool. AI-GFR estimation offers the possibility of noninvasive assessment of kidney function, a key goal of AI-powered functional automation in clinical practice.

Subfields: Pediatrics, Perinatology and Child Health, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Greedy AutoAugment (2019, background): “In this case, gathering sufficient labeled data to train a high capacity deep model is impractical [1], [2].”
A Review of Deep Learning on Medical Image Analysis (2020, methodology): “[114] proposed their transfer learning-based method blended with several deep learning schemes for intelligently predicting the estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) status.”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “Kidney function can be predicted using deep learning of ultrasound images [195].”