2026-04-17T14:45:02Zhttps://recercat.cat/oai/request

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

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD Taylor, Jonathan Thomas, Richard Metherall, Peter van Gastel, Marieke Cornec-Le Gall, Emilie Caroli, Anna Furlano, Monica Demoulin, Nathalie Devuyst, Olivier Winterbottom, Jean Torra Balcells, Roser Perico, Norberto Le Meur, Yannick Schoenherr, Sebastian Forer, Lukas Gansevoort, Ron T. Simms, Roslyn J. Ong, Albert C. M. Universitat Autònoma de Barcelona ADPKD Artificial intelligence Machine learning Magnetic resonance imaging Total kidney volume 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. 2023 Article Kidney International Reports ; Vol. 9 (november 2023), p. 249-256 open access Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. https://creativecommons.org/licenses/by-nc-nd/4.0/