2026-04-13T12:32:53Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4752172025-10-13T11:37:55Zcom_2072_98col_2072_378192

00925njm 22002777a 4500 dc Taylor, Jonathan author Thomas, Richard author Metherall, Peter author van Gastel, Marieke author Cornec-Le Gall, Emilie author Caroli, Anna author Furlano, Monica author Demoulin, Nathalie author Devuyst, Olivier author Winterbottom, Jean author Torra Balcells, Roser author Perico, Norberto author Le Meur, Yannick author Schoenherr, Sebastian author Forer, Lukas author Gansevoort, Ron T. author Simms, Roslyn J. author Ong, Albert C. M. author Universitat Autònoma de Barcelona author 2023 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). 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. 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. 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. ADPKD Artificial intelligence Machine learning Magnetic resonance imaging Total kidney volume An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD