Neural network calibration for radiotherapy
Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.
1. A method comprising:
retrieving, by one or more processors via a radiation therapy treatment generation (RTTP) model, a set of treatment attribute predictions for a radiotherapy treatment of a patient;
executing, by the one or more processors, a calibration model using the set of treatment attribute predictions and a set of labels indicating expected treatment attribute predictions to predict a calibration value; and
revising, by the one or more processors, at least one configuration of the RTTP model using the calibration value.
2. The method of claim 1 , further comprising:
responsive to revising the at least one configuration of the RTTP model, executing, by the one or more processors, the calibration model to predict a second calibration value.
3. The method of claim 1 , wherein the RTTP model is initially partially trained and then calibrated using the calibration model.
4. The method of claim 1 , wherein the calibration value corresponds to a clinic, such that the RTTP model is trained for the clinic.
5. The method of claim 1 , further comprising:
adjusting, by the one or more processors, a configuration of a radiotherapy machine according to at least one treatment attribute prediction generated by the RTTP model.
6. The method of claim 1 , wherein the calibration model is one of a machine learning model or a mathematical optimization algorithm.
7. The method of claim 1 , further comprising:
generating, by the one or more processors, a confidence score for the set of treatment attribute predictions generated using the RTTP model.
8. The method of claim 1 , wherein at least one treatment attribute prediction within the set of treatment attribute predictions is an angle associated with a couch or a gantry of a radiotherapy machine.
9. A system comprising:
at least one processor;
a computer readable medium comprising a set of instructions, that when executed, cause the at least one processor to:
retrieve, via a radiation therapy treatment generation (RTTP) model, a set of treatment attribute predictions for a radiotherapy treatment of a patient;
execute a calibration model using the set of treatment attribute predictions and a set of labels indicating expected treatment attribute predictions to predict a calibration value; and
revise at least one configuration of the RTTP model using the calibration value.
10. The system of claim 9 , wherein the set of instructions further cause the at least one processor to:
responsive to revising the at least one configuration of the RTTP model, execute the calibration model to predict a second calibration value.
11. The system of claim 9 , wherein the RTTP model is initially partially trained and then calibrated using the calibration model.
12. The system of claim 9 , wherein the calibration value corresponds to a clinic, such that the RTTP model is trained for the clinic.
13. The system of claim 9 , wherein the set of instructions further cause the at least one processor to:
adjust a configuration of a radiotherapy machine according to at least one treatment attribute prediction generated by the RTTP model.
14. The system of claim 9 , wherein the calibration model is one of a machine learning model or a mathematical optimization algorithm.
15. The system of claim 9 , wherein the set of instructions further cause the at least one processor to:
generate a confidence score for the set of treatment attribute predictions generated using the RTTP model.
16. The system of claim 9 , wherein at least one treatment attribute prediction within the set of treatment attribute predictions is an angle associated with a couch or a gantry of a radiotherapy machine.
17. A system comprising:
a radiation therapy treatment generation (RTTP) model;
a calibration model;
a server in communication with the RTTP model and the calibration model, the server configured to:
retrieve, via the RTTP model, a set of treatment attribute predictions for a radiotherapy treatment of a patient;
execute the calibration model using the set of treatment attribute predictions and a set of labels indicating expected treatment attribute predictions to predict a calibration value; and
revise at least one configuration of the RTTP model using the calibration value.
18. The system of claim 17 , wherein the server is further configured to:
responsive to revising the at least one configuration of the RTTP model, execute the calibration model to predict a second calibration value.
19. The system of claim 17 , wherein the RTTP model is initially partially trained and then calibrated using the calibration model.
20. The system of claim 17 , wherein the calibration value corresponds to a clinic, such that the RTTP model is trained for the clinic.