Machine-learning system for diagnosing disorders and diseases and determining drug responsiveness
AssigneeSanford Burnham Prebys Medical Discovery Institute
InventorsCameron Pernia, Heather Tolcher, Evan Y. Snyder
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Abstract
Described are platforms, systems, and methods for screening patients. In one aspect, a computer-implemented method comprises: receiving, from a cellular imaging device, image data comprising calcium kinetic features of neuronal cultures derived from a patient; processing the image data through a machine-learning model to determine a diagnosis for the patient based on the calcium kinetic features, the machine-learning model trained using neuronal calcium data; and providing the diagnosis a user interface.
Claims15
A classification system, comprising:
- a user interface;
- a cellular imaging device;
- one or more processors;
- and
- a non-transitory computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- receiving, from the cellular imaging device, image data comprising calcium kinetic features of neuronal cultures derived from a patient;
- processing the image data through a machine-learning model to determine a diagnosis for the patient based on the calcium kinetic features, wherein the machine-learning model is trained using neuronal calcium data;
- and
- wherein the neuronal calcium data comprises (i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux;
- and
- providing the diagnosis to the user interface, wherein the diagnosis is for bipolar disorder (BPD), Alzheimer's disease or Parkinson's disease.
A computer-implemented method for patient screening, the method being executed by one or more processors and comprising:
- receiving, from a cellular imaging device, image data comprising calcium kinetic features of neuronal cultures derived from a patient;
- processing the image data through a machine-learning model to determine a diagnosis for the patient based on the calcium kinetic features, wherein the machine-learning model is trained using neuronal calcium data and wherein the neuronal calcium data comprises (i) basal calcium level, peak calcium transience, calcium event frequency, and calcium event amplitude, and (ii) at least one of calcium event influx and calcium event efflux;
- and
- providing the diagnosis to a user interface, wherein the diagnosis is for bipolar disorder (BPD), Alzheimer's disease or Parkinson's disease.
Dependencies are inferred from the claim text.