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...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
04 January 2022
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| 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
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| Verfasserangaben: | Cleo-Aron Weis, Jan Niklas Bindzus, Jonas Voigt, Marlen Runz, Svetlana Hertjens, Matthias M. Gaida, Zoran V. Popovic, Stefan Porubsky |
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| 520 | |a 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. | ||
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