Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application of computer-aided diagnosis (CAD) challenging. This study assessed the effect of integrating computer-extracted measurable features with the convolutional neural network (CNN) on the ultrasound image CAD accuracy of CKD.
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