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Verfasst von:Raj, Anish [VerfasserIn]   i
 Tollens, Fabian [VerfasserIn]   i
 Caroli, Anna [VerfasserIn]   i
 Nörenberg, Dominik [VerfasserIn]   i
 Zöllner, Frank G. [VerfasserIn]   i
Titel:Automated prognosis of renal function decline in ADPKD patients using deep learning
Verf.angabe:Anish Raj, Fabian Tollens, Anna Caroli, Dominik Nörenberg, Frank G. Zöllner
E-Jahr:2024
Jahr:May 2024
Umfang:13 S.
Illustrationen:Illustrationen
Fussnoten:Online verfügbar: 21. August 2023, Artikelversion: 24. Mai 2024 ; Gesehen am 15.11.2024
Titel Quelle:Enthalten in: Zeitschrift für medizinische Physik
Ort Quelle:Amsterdam [u.a.] : Elsevier, 1990
Jahr Quelle:2024
Band/Heft Quelle:34(2024), 2 vom: Mai, Seite 330-342
ISSN Quelle:1876-4436
Abstract:An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages.
DOI:doi:10.1016/j.zemedi.2023.08.001
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1016/j.zemedi.2023.08.001
 kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S0939388923000909
 DOI: https://doi.org/10.1016/j.zemedi.2023.08.001
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Autosomal dominant polycystic kidney disease
 Chronic kidney disease
 Deep learning
 Image classification
 Regression
 Total kidney volume
K10plus-PPN:1908767103
Verknüpfungen:→ Zeitschrift

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