Abstract
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.
Highlights
The main clinical application of kidney ultrasound imaging involves excluding reversible causes of acute kidney injury, such as urinary obstruction, or identifying irreversible chronic kidney disease (CKD) that precludes unnecessary workup such as kidney biopsy.[1]
For classifying estimated glomerular filtration rate (eGFR) with a threshold of 60 ml/min/1.73 m2, our model achieved an overall accuracy of 85.6% and area under receiver operating characteristic (ROC) curve (AUC) of 0.904
The proposed algorithm moderately predicts continuous eGFR. It can reliably determine whether eGFR is below 60 ml/min/1.73 m2, with an accuracy superior to that of senior nephrologists
Summary
The main clinical application of kidney ultrasound imaging involves excluding reversible causes of acute kidney injury, such as urinary obstruction, or identifying irreversible chronic kidney disease (CKD) that precludes unnecessary workup such as kidney biopsy.[1] Its noninvasiveness, low cost, lack of ionizing radiation, and wide availability make it an attractive option for frequent monitoring and follow-up of the longitudinal change in kidney length and sonographic characteristics of kidney cortex relevant to kidney functional change. Studies[3,4,5,6,7,8,9,10,11,12,13,14] have reported that kidney length is highly specific in detecting irreversible CKD, its correlation with eGFR was only weak to moderate, ranging no association to 0.66. Even if only studies using the conventional Modification of Diet in
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