Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.

ABSTRACT

This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P < .001), from 1724 seconds (95% CI 1373, 2075) to 723 seconds (95% CI 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time. Keywords Convolutional Neural Network (CNN), Segmentation, Kidney ClinicalTrials.gov identification no. NCT00792155 Supplemental material is available for this article. © RSNA, 2022.