SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT GENETIC MUTATION

1 . A method comprising:

receiving, by a processor, an image of a tumor of a patient;

executing, by the processor, a first model to identify one or more visual attributes of the tumor using the image of the tumor as input;

executing, by the processor, a second model to predict a genetic mutation of the patient using the one or more visual attributes as input; and

identifying, by the processor, a therapy protocol associated with the tumor based on the genetic mutation.

2 . The method of claim 1 , the first model is a machine learning model trained based on historical data of a plurality of images of tumors, each of the plurality of images of tumors associated with a list of one or more visual attributes.

3 . The method of claim 1 , wherein executing the second model comprises comparing, by the processor, the one or more visual attributes to one or more templates, each template corresponding to a different type of genetic mutation.

4 . The method of claim 1 , wherein executing the second model comprises:

retrieving, by the processor, patient attributes of the patient from a database; and

executing, by the processor, the second model using the one or more visual attributes and the patient attributes as input.

5 . The method of claim 4 , wherein executing the second model comprises:

determining, by the processor, a confidence score for the genetic mutation based on the one or more visual attributes and the patient attributes; and

selecting, by the processor, the genetic mutation based on the confidence score.

6 . The method of claim 1 , further comprising:

adding, by the processor, an identification of the patient to a list of patients for the therapy protocol.

7 . The method of claim 1 , wherein the therapy protocol is a first therapy protocol, and further comprising:

executing, by the processor using one or more patient attributes of the patient as input, a third model to predict a molecular alteration for the patient; and

determining, by the processor, the first therapy protocol for the patient responsive to the molecular alteration corresponding to a second therapy protocol.

8 . The method of claim 7 , wherein the molecular alteration indicates a resistance to the first therapy protocol.

9 . The method of claim 7 , wherein the third model is a nodal data structure.

10 . A system comprising:

a processor configured to:

receive an image of a tumor of a patient;

execute a first model to identify one or more visual attributes of the tumor using the image of the tumor as input;

execute a second model to predict a genetic mutation of the patient using the one or more visual attributes as input; and

identify a therapy protocol associated with the tumor based on the genetic mutation.

11 . The system of claim 10 , the first model is a machine learning model trained based on historical data of a plurality of images of tumors, each of the plurality of images of tumors associated with a list of one or more visual attributes.

12 . The system of claim 10 , wherein to execute the second model, the processor is configured to compare the one or more visual attributes to one or more templates, each template corresponding to a different type of genetic mutation.

13 . The system of claim 10 , wherein to execute the second model, the processor is configured to:

retrieve patient attributes of the patient from a database; and

execute the second model using the one or more visual attributes and the patient attributes as input.

14 . The system of claim 13 , wherein to execute the second model, the processor is configured to:

determine a confidence score for the genetic mutation based on the one or more visual attributes and the patient attributes; and

select the genetic mutation based on the confidence score.

15 . The system of claim 10 , wherein the processor is further configured to:

add an identification of the patient to a list of patients for the therapy protocol.

16 . The system of claim 10 , wherein the therapy protocol is a first therapy protocol, and the processor is further configured to:

execute, using one or more patient attributes of the patient as input, a third model to predict a molecular alteration for the patient; and

determine the first therapy protocol for the patient responsive to the molecular alteration corresponding to a second therapy protocol.

17 . The system of claim 16 , wherein the molecular alteration indicates a resistance to the first therapy protocol.

18 . A method comprising:

receiving, by a processor, clinical information of a patient;

identifying, by the processor, one or more features from an image of a tumor of the patient;

predicting, by the processor, using a model, a genetic mutation of the patient according to the one or more features; and

determining, by the processor, a therapy protocol according to the clinical information and the genetic mutation of the patient.

19 . The method of claim 18 , wherein the genetic mutation is a first genetic mutation, and wherein determining the therapy protocol comprises:

predicting, by the processor, using the model, a second genetic mutation of the patient according to the one or more features; and

determining, by the processor, the therapy protocol according to a combination of the first genetic mutation and the second genetic mutation and the clinical information.

20 . The method of claim 18 , wherein predicting a genetic mutation and determining the therapy protocol comprises:

predicting, by the processor, using the model, more than two genetic mutations of the patient according to the one or more features; and

determining, by the processor, chemotherapy as the therapy protocol according to more than two genetic mutations associated with the tumor of the patient.