Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.

TitleDeployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.
Publication TypeJournal Article
Year of Publication2022
AuthorsGoel A, Shih G, Riyahi S, Jeph S, Dev H, Hu R, Romano D, Teichman K, Blumenfeld JD, Barash I, Chicos I, Rennert H, Prince MR
JournalRadiol Artif Intell
Date Published2022 Mar

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 identification no.: NCT00792155 Supplemental material is available for this article. © RSNA, 2022.

Alternate JournalRadiol Artif Intell
PubMed ID35391774
PubMed Central IDPMC8980881
Grant ListUL1 TR002384 / TR / NCATS NIH HHS / United States
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