US 12,322,101 B2
Methods of analyzing microscopy images using machine learning
John A. Stamatoyannopoulos, Seattle, WA (US); Shreeram Akilesh, Seattle, WA (US); Alexander Muratov, Seattle, WA (US); Wouter Meuleman, Seattle, WA (US); and William Kerwin, Seattle, WA (US)
Assigned to Altius Institute for Biomedical Sciences, Seattle, WA (US)
Filed by Altius Institute for Biomedical Sciences, Seattle, WA (US)
Filed on Jan. 5, 2024, as Appl. No. 18/405,710.
Application 18/405,710 is a continuation of application No. 18/045,342, filed on Oct. 10, 2022, granted, now 11,900,600.
Application 18/045,342 is a continuation of application No. 16/630,090, granted, now 11,501,429, issued on Nov. 15, 2022, previously published as PCT/US2018/042964, filed on Jul. 19, 2018.
Claims priority of provisional application 62/534,679, filed on Jul. 19, 2017.
Prior Publication US 2024/0242340 A1, Jul. 18, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06N 20/00 (2019.01); G16B 40/20 (2019.01); G16B 40/30 (2019.01)
CPC G06T 7/0012 (2013.01) [G06N 20/00 (2019.01); G16B 40/20 (2019.02); G16B 40/30 (2019.02); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for identifying a genetic, epigenetic, or genomic trait in a cell sample, the method comprising:
a) capturing a series of one or more images of the cell sample; and
b) processing the series of one or more images using a machine learning algorithm to identify one or more cell phenotypic traits that are correlated with the genetic, epigenetic, or genomic trait;
wherein the machine learning algorithm has been trained using a training data set that comprises cell image data and nucleic acid sequence data.