Quality assurance process for radiation therapy treatment planning
AssigneeVarian Medical Systems, Inc.
InventorCharles Adelsheim
Life of this patent
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Abstract
A method enables testing and evaluation of an expert human reviewer or an artificial intelligence (AI) error detection engine associated with a radiotherapy treatment planning process. Intentional errors are introduced into the output of a software module or AI engine that performs a certain step in the radiotherapy treatment planning process. The efficacy of the human or AI reviewer in detecting errors can then be evaluated or tested by determining whether the human or AI reviewer has detected the introduced error.
Claims20
A computer-implemented method for quality assurance testing of a treatment planning process, the method comprising:
- receiving output data from a module of the treatment planning process, wherein the output data is based on input data for the module;
- introducing at least one intentional error into the output data;
- submitting the output data with the at least one intentional error to a reviewer for an error check, wherein the reviewer comprises a machine learning process configured to check an output of the module for errors;
- receiving an error check response associated with the reviewer;
- and
- when the error check response indicates a failure to detect the at least one intentional error, retraining the machine learning process based on the at least one intentional error and on normal output data from the module that is generated from the input data.
A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, configure the one or more processors to perform the steps of:
- receiving output data from a module of the treatment planning process, wherein the output data is based on input data for the module;
- introducing at least one intentional error into the output data;
- submitting the output data with the at least one intentional error to a reviewer for an error check, wherein the reviewer comprises a machine learning process configured to check an output of the module for errors;
- receiving an error check response associated with the reviewer;
- and
- when the error check response indicates a failure to detect the at least one intentional error, retraining the machine learning process based on the at least one intentional error and on normal output data from the module that is generated from the input data.
Dependencies are inferred from the claim text.