Taylor, J., Thomas, R., Metherall, P. et al. (15 more authors) (2024) An artificial intelligence generated automated algorithm to measure total kidney volume in ADPKD. Kidney International Reports, 9 (2). pp. 249-256. ISSN 2468-0249
Abstract
Introduction
Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI) generated method for routinely measuring total kidney volume (TKV).
Methods
An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T MRI data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium which was first manually segmented by a single human operator. As an independent validation cohort, we utilised 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single centre. The tool was then implemented for clinical use and its performance analysed.
Results
The training / internal validation cohort was younger (mean age 44.0 vs 51.5 years) and the female-male ratio higher (1.2 v 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging Class 1, 86%). The median DICE score on the clinical validation dataset between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic dataset was 56 (±28) min whereas manual corrections of the algorithm output took 8.5 (±9.2) min per scan.
Conclusions
Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Clinical Research; Kidney Disease; Bioengineering; Polycystic Kidney Disease; Biomedical Imaging; Renal and urogenital |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number SHEFFIELD HOSPITALS CHARITY 141515-3 SHEFFIELD HOSPITALS CHARITABLE TRUST 192025 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2023 16:02 |
Last Modified: | 09 Oct 2024 16:02 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ekir.2023.10.029 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205459 |