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AI-enabled in silico immunohistochemical characterization for Alzheimer's disease

We develop a deep learning approach, in silico immunohistochemistry (IHC), which takes routinely collected histochemical-stained samples as input and computationally generates virtual IHC slide images. We apply in silico IHC to Alzheimer's disease samples, where several hallmark changes are con...

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Detalles Bibliográficos
Autores principales: He, Bryan, Bukhari, Syed, Fox, Edward, Abid, Abubakar, Shen, Jeanne, Kawas, Claudia, Corrada, Maria, Montine, Thomas, Zou, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046239/
https://www.ncbi.nlm.nih.gov/pubmed/35497493
http://dx.doi.org/10.1016/j.crmeth.2022.100191
Descripción
Sumario:We develop a deep learning approach, in silico immunohistochemistry (IHC), which takes routinely collected histochemical-stained samples as input and computationally generates virtual IHC slide images. We apply in silico IHC to Alzheimer's disease samples, where several hallmark changes are conventionally identified using IHC staining across many regions of the brain. In silico IHC computationally identifies neurofibrillary tangles, β-amyloid plaques, and neuritic plaques at a high spatial resolution directly from the histochemical images, with areas under the receiver operating characteristic curve of between 0.88 and 0.92. In silico IHC learns to identify subtle cellular morphologies associated with these lesions and can generate in silico IHC slides that capture key features of the actual IHC.