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, 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 output a calibration value;
executing, by the one or more processors, an artificial intelligence model using a set of characteristics of the patient to output a confidence score associated with at least one treatment attribute prediction of the set of treatment attribute predictions; and
adjusting, by the one or more processors, at least one confidence score according to the calibration value.
2. The method of claim 1 , wherein the set of treatment attribute predictions is received from the artificial intelligence model.
3. The method of claim 1 , further comprising:
executing, by the one or more processors, the artificial intelligence model using a set of characteristics of a second patient to output a second confidence score associated with a second treatment attribute prediction and adjusting the second confidence score according to the calibration value to predict a revised second treatment attribute prediction.
4. 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.
5. The method of claim 1 , further comprising:
presenting, by the one or more processors for display, the adjusted confidence score and the first treatment attribute.
6. The method of claim 5 , further comprising:
responsive to receiving the user input, adjusting, by the one or more processors, a configuration of the radiotherapy machine.
7. The method of claim 5 , further comprising:
responsive to receiving the user input, adjusting, by the one or more processors, a configuration of the radiotherapy machine.
8. The method of claim 1 , further comprising:
adjusting, by the one or more processors, the artificial intelligence model in accordance with the calibration value.
9. The method of claim 1 , wherein the treatment attribute is an angle associated with a couch or a gantry of a radiotherapy machine.
10. The method of claim 1 , wherein the calibration model is one of a machine learning model or a mathematical optimization algorithm.
11. A system comprising:
a non-transitory computer readable medium comprising a set of instructions, that when executed, cause at least one processor to:
retrieve 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 output a calibration value;
execute an artificial intelligence model using a set of characteristics of the patient to output a confidence score associated with at least one treatment attribute prediction of the set of treatment attribute predictions; and
adjust at least one confidence score according to the calibration value.
12. The system of claim 11 , wherein the set of treatment attribute predictions is received from the artificial intelligence model.
13. The system of claim 11 , wherein the instructions further cause the processor to:
execute the artificial intelligence model using a set of characteristics of a second patient to output a second confidence score associated with a second treatment attribute prediction and adjusting the second confidence score according to the calibration value to predict a revised second treatment attribute prediction.
14. The system of claim 11 , wherein the instructions further cause the processor to:
adjust a configuration of a radiotherapy machine according to at least one treatment attribute prediction.
15. The system of claim 11 , wherein the instructions further cause the processor to:
present, for display, the adjusted confidence score and the first treatment attribute.
16. The system of claim 15 , wherein the instructions further cause the processor to:
responsive to receiving the user input, adjust a configuration of the radiotherapy machine.
17. The system of claim 15 , wherein the instructions further cause the processor to:
responsive to receiving the user input, adjust a configuration of the radiotherapy machine.
18. The system of claim 11 , wherein the instructions further cause the processor to:
adjust the artificial intelligence model in accordance with the calibration value.
19. The system of claim 11 , wherein the treatment attribute is an angle associated with a couch or a gantry of a radiotherapy machine.
20. The system of claim 11 , wherein the calibration model is one of a machine learning model or a mathematical optimization algorithm.