US 12,277,711 B2
Cellular time-series imaging, modeling, and analysis system
Herve Marie-Nelly, San Carlos, CA (US); Jeevaa Velayutham, Selangor (MY); Zachary Phillips, San Francisco, CA (US); and Shengjiang Tu, Foster City, CA (US)
Assigned to Insitro, Inc., South San Francisco, CA (US)
Filed by Insitro, Inc., South San Francisco, CA (US)
Filed on May 17, 2024, as Appl. No. 18/667,956.
Application 18/667,956 is a continuation of application No. 18/666,672, filed on May 16, 2024.
Claims priority of provisional application 63/467,582, filed on May 18, 2023.
Prior Publication US 2024/0395415 A1, Nov. 28, 2024
Int. Cl. G06T 7/00 (2017.01); G06V 10/762 (2022.01); G06V 10/77 (2022.01); G06V 20/69 (2022.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)
CPC G06T 7/0016 (2013.01) [G06V 10/762 (2022.01); G06V 20/695 (2022.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06V 10/77 (2022.01); G06V 20/698 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system for modeling a progression of a disease, the system comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
obtaining a first plurality of sets of time-series image data of one or more first live biological cells, wherein at least some of the one or more first live biological cells express a first disease state of the disease;
determining a first plurality of sequences of embeddings by inputting the first plurality of sets of time-series image data into a trained machine learning model;
determining a first plurality of summary embeddings based on the first plurality of sequences of embeddings, wherein a summary embedding of the first plurality of summary embeddings comprises a temporal dimension based on temporal information associated with a first sequence of embeddings in the first plurality of sequences of embeddings;
obtaining a second plurality of sets of time-series image data of one or more second live biological cells, wherein at least some of the one or more second live biological cells express a second disease state of the disease;
determining a second plurality of sequences of embeddings by inputting the second plurality of sets of time-series image data into the trained machine learning model;
determining a second plurality of summary embeddings based on the second plurality of sequences of embeddings, wherein a summary embedding of the second plurality of summary embeddings comprises a temporal dimension based on temporal information associated with a second sequence of embeddings in the second plurality of sequences of embeddings;
generating a disease model representing a plurality of disease states in a topological space based on the first plurality of summary embeddings and the second plurality of summary embeddings; and
modeling a progression of the disease based on the disease model.