A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence.
Nat Commun
; 15(1): 5112, 2024 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-38879641
ABSTRACT
Virus infectivity is traditionally determined by endpoint titration in cell cultures, and requires complex processing steps and human annotation. Here we developed an artificial intelligence (AI)-powered automated framework for ready detection of virus-induced cytopathic effect (DVICE). DVICE uses the convolutional neural network EfficientNet-B0 and transmitted light microscopy images of infected cell cultures, including coronavirus, influenza virus, rhinovirus, herpes simplex virus, vaccinia virus, and adenovirus. DVICE robustly measures virus-induced cytopathic effects (CPE), as shown by class activation mapping. Leave-one-out cross-validation in different cell types demonstrates high accuracy for different viruses, including SARS-CoV-2 in human saliva. Strikingly, DVICE exhibits virus class specificity, as shown with adenovirus, herpesvirus, rhinovirus, vaccinia virus, and SARS-CoV-2. In sum, DVICE provides unbiased infectivity scores of infectious agents causing CPE, and can be adapted to laboratory diagnostics, drug screening, serum neutralization or clinical samples.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Efecto Citopatogénico Viral
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SARS-CoV-2
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Microscopía
Límite:
Animals
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Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Suiza