An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD
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...
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Elsevier
2024-02-01
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| Series: | Kidney International Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468024923015711 |
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| author | Jonathan Taylor Richard Thomas Peter Metherall Marieke van Gastel Emilie Cornec-Le Gall Anna Caroli Monica Furlano Nathalie Demoulin Olivier Devuyst Jean Winterbottom Roser Torra Norberto Perico Yannick Le Meur Sebastian Schoenherr Lukas Forer Ron T. Gansevoort Roslyn J. Simms Albert C.M. Ong |
| author_facet | Jonathan Taylor Richard Thomas Peter Metherall Marieke van Gastel Emilie Cornec-Le Gall Anna Caroli Monica Furlano Nathalie Demoulin Olivier Devuyst Jean Winterbottom Roser Torra Norberto Perico Yannick Le Meur Sebastian Schoenherr Lukas Forer Ron T. Gansevoort Roslyn J. Simms Albert C.M. Ong |
| author_sort | Jonathan Taylor |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8d207eef50c940349d6429e450b9a25e |
| institution | DOAJ |
| issn | 2468-0249 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kidney International Reports |
| spelling | doaj-art-8d207eef50c940349d6429e450b9a25e2025-08-20T03:20:30ZengElsevierKidney International Reports2468-02492024-02-019224925610.1016/j.ekir.2023.10.029An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKDJonathan Taylor0Richard Thomas1Peter Metherall2Marieke van Gastel3Emilie Cornec-Le Gall4Anna Caroli5Monica Furlano6Nathalie Demoulin7Olivier Devuyst8Jean Winterbottom9Roser Torra10Norberto Perico11Yannick Le Meur12Sebastian Schoenherr13Lukas Forer14Ron T. Gansevoort15Roslyn J. Simms16Albert C.M. Ong173DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UKDepartment of Nephrology, University Medical Centre Groningen, Groningen, The NetherlandsUniversity Brest, Inserm, UMR 1078, GGB, CHU Brest, F-29200 Brest, FranceIstituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, ItalyInherited Kidney Disorders, Nephrology Department, Fundació Puigvert, IIB Sant Pau, Universitat Autònoma de Barcelona, Barcelona, SpainCliniques Universitaires Saint-Luc, UCLouvain Medical School, Brussels, BelgiumCliniques Universitaires Saint-Luc, UCLouvain Medical School, Brussels, BelgiumAcademic 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, UKInherited Kidney Disorders, Nephrology Department, Fundació Puigvert, IIB Sant Pau, Universitat Autònoma de Barcelona, Barcelona, SpainIstituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, ItalyUniversity Brest, Inserm, UMR 1227, LBAI, CHU Brest, F-29200 Brest, FranceInstitute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, AustriaInstitute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, AustriaDepartment of Nephrology, University Medical Centre Groningen, Groningen, The NetherlandsAcademic 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; Correspondence: Roslyn J. Simms or Albert C.M. Ong, Academic Nephrology, Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK.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; Correspondence: Roslyn J. Simms or Albert C.M. Ong, Academic Nephrology, Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK.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.http://www.sciencedirect.com/science/article/pii/S2468024923015711ADPKDartificial intelligencemachine learningmagnetic resonance imagingtotal kidney volume |
| spellingShingle | Jonathan Taylor Richard Thomas Peter Metherall Marieke van Gastel Emilie Cornec-Le Gall Anna Caroli Monica Furlano Nathalie Demoulin Olivier Devuyst Jean Winterbottom Roser Torra Norberto Perico Yannick Le Meur Sebastian Schoenherr Lukas Forer Ron T. Gansevoort Roslyn J. Simms Albert C.M. Ong An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD Kidney International Reports ADPKD artificial intelligence machine learning magnetic resonance imaging total kidney volume |
| title | An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD |
| title_full | An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD |
| title_fullStr | An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD |
| title_full_unstemmed | An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD |
| title_short | An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD |
| title_sort | artificial intelligence generated automated algorithm to measure total kidney volume in adpkd |
| topic | ADPKD artificial intelligence machine learning magnetic resonance imaging total kidney volume |
| url | http://www.sciencedirect.com/science/article/pii/S2468024923015711 |
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