An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD.

Autor:	Taylor J; 3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK., Thomas R; 3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK., Metherall P; 3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK., van Gastel M; Department of Nephrology, University Medical Centre Groningen, Groningen, The Netherlands., Cornec-Le Gall E; University Brest, Inserm, UMR 1078, GGB, CHU Brest, F-29200 Brest, France., Caroli A; Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy., Furlano M; Inherited Kidney Disorders, Nephrology Department, Fundació Puigvert, IIB Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain., Demoulin N; Cliniques Universitaires Saint-Luc, UCLouvain Medical School, Brussels, Belgium., Devuyst O; Cliniques Universitaires Saint-Luc, UCLouvain Medical School, Brussels, Belgium., Winterbottom J; Academic Nephrology, Division of Clinical Medicine, School of Medicine and Population Health, Faculty of Health, University of Sheffield, Sheffield, UK.; Sheffield Kidney Institute, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK., Torra R; Inherited Kidney Disorders, Nephrology Department, Fundació Puigvert, IIB Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain., Perico N; Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy., Le Meur Y; University Brest, Inserm, UMR 1227, LBAI, CHU Brest, F-29200 Brest, France., Schoenherr S; Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Austria., Forer L; Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Austria., Gansevoort RT; Department of Nephrology, University Medical Centre Groningen, Groningen, The Netherlands., Simms RJ; Academic Nephrology, Division of Clinical Medicine, School of Medicine and Population Health, Faculty of Health, University of Sheffield, Sheffield, UK.; Sheffield Kidney Institute, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK., Ong ACM; Academic Nephrology, Division of Clinical Medicine, School of Medicine and Population Health, Faculty of Health, University of Sheffield, Sheffield, UK.; Sheffield Kidney Institute, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Jazyk:	angličtina
Zdroj:	Kidney international reports [Kidney Int Rep] 2023 Nov 04; Vol. 9 (2), pp. 249-256. Date of Electronic Publication: 2023 Nov 04 (Print Publication: 2024).
DOI:	10.1016/j.ekir.2023.10.029
Abstrakt:	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 magnetic resonance imaging (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 utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 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 data set 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 data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Conclusion: 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. (Crown Copyright © 2023 Published by Elsevier Inc. on behalf of the International Society of Nephrology.)
Databáze:	MEDLINE
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