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• W3137450897 abstract "ABSTRACT Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an AI classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity (PICS). Training on nine thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques." @default.
• W3137450897 created "2021-03-29" @default.
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• W3137450897 date "2021-03-23" @default.
• W3137450897 modified "2023-09-27" @default.
• W3137450897 title "Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)" @default.
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• W3137450897 doi "https://doi.org/10.1101/2021.03.22.436473" @default.
• W3137450897 hasPublicationYear "2021" @default.
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