High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.
Sci Rep
; 13(1): 6361, 2023 04 19.
Article
em En
| MEDLINE
| ID: mdl-37076596
ABSTRACT
Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about heterogeneous injury responses, but often only a fraction of stained tissue is analyzed. Deep learning can expand analysis to larger areas and sample numbers by substituting for time-intensive manual or semi-automated quantification techniques. Here we report one approach to leverage deep learning tools to quantify heterogenous responses to kidney injury that can be deployed without specialized equipment or programming expertise. We first demonstrated that deep learning models generated from small training sets accurately identified a range of stains and structures with performance similar to that of trained human observers. We then showed this approach accurately tracks the evolution of folic acid induced kidney injury in mice and highlights spatially clustered tubules that fail to repair. We then demonstrated that this approach captures the variation in recovery across a robust sample of kidneys after ischemic injury. Finally, we showed markers of failed repair after ischemic injury were correlated both spatially within and between animals and that failed repair was inversely correlated with peritubular capillary density. Combined, we demonstrate the utility and versatility of our approach to capture spatially heterogenous responses to kidney injury.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Injúria Renal Aguda
/
Aprendizado Profundo
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos