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Assessment of glomerular morphological patterns by deep learning algorithms

Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangi...

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Hauptverfasser: Weis, Cleo-Aron Thias (VerfasserIn) , Bindzus, Jan Niklas (VerfasserIn) , Voigt, Jonas (VerfasserIn) , Runz, Marlen (VerfasserIn) , Hetjens, Svetlana (VerfasserIn) , Gaida, Matthias (VerfasserIn) , Popovic, Zoran V. (VerfasserIn) , Porubský, Štefan (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 04 January 2022
In: Journal of nephrology
Year: 2022, Jahrgang: 35, Heft: 2, Pages: 417-427
ISSN:1724-6059
DOI:10.1007/s40620-021-01221-9
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s40620-021-01221-9
Volltext
Verfasserangaben:Cleo-Aron Weis, Jan Niklas Bindzus, Jonas Voigt, Marlen Runz, Svetlana Hertjens, Matthias M. Gaida, Zoran V. Popovic, Stefan Porubsky
Beschreibung
Zusammenfassung:Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology.
Beschreibung:Gesehen am 07.02.2022
Beschreibung:Online Resource
ISSN:1724-6059
DOI:10.1007/s40620-021-01221-9

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