Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

The present disclosure provides methods for user adaptive radiation therapy planning and a system for using the same. In some aspect, method for generating a radiation therapy plan is provided. The method includes receiving imaging information acquired from a patient and producing a preliminary radiation therapy plan, using a treatment planning system, based on the imaging information. The method also includes generating an indication for modifying the preliminary radiation therapy plan in accordance with a predetermined clinician profile, wherein the predetermined clinician profile is based on a trained learning machine. The method further includes producing, using the indication, an updated radiation therapy plan that is adapted to the predetermined clinician profile.

Apparatus for planning a diffusing alpha-emitter radiation therapy (DaRT) treatment session. The apparatus includes an output interface and a memory configured with a plurality of tables which provide an accumulated measure of radiation over a specific time period, due to one or more types of DaRT radiotherapy sources which emit daughter radionuclides from the source, for a plurality of different distances and angles relative to the DaRT radiotherapy source. In addition, a processor is configured to receive a description of a layout of a plurality of DaRT radiotherapy sources in a tumor, to calculate a radiation dose distribution in the tumor responsive to the layout, using the tables in the memory, and to output feedback for the treatment responsive to the radiation dose distribution, through the output interface.

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

To provide a treatment plan evaluation system capable of supporting determination when a treatment plan is selected. A treatment plan evaluation system 1 that evaluates a treatment plan of a radiotherapy system RTS has a function of comparing a selected treatment plan with a record in which adoption of a past treatment plan has been determined, and provides a comparison result between the selected treatment plan and the record.

A set of baseline treatment information is received. From the set of baseline treatment information, a set of incidental information is determined. Based on the set of baseline treatment information and the set of incidental information, a treatment mitigation area is generated. Based on the set of baseline treatment information, the set of incidental information, and the treatment mitigation area, a treatment mitigation model is created. An affected individual is identified, based on the treatment mitigation model. The affected individual is notified of one or more treatment mitigation recommendations.

A system and method are provided for assessing a radiation therapy plan to be implemented on a particular radiation therapy system that includes a multi-leaf collimator (MLC). The method includes receiving a radiation therapy plan and calculating at least one metric indicating transmission characteristics of a beam delivered using the particular radiation therapy system to perform the radiation therapy plan using a model of the MLC having a plurality of zones, wherein each zone is classified based on the transmission characteristics. The method also includes evaluating the at least one metric against a tolerance for variation between the radiation therapy plan and an implementation of the radiation therapy plan on the particular radiation therapy system and generating an alert indicating that the at least one metric is outside the tolerance.

Radiation treatment planning for charged particle therapy systems, such as proton therapy systems, includes calculating dose distribution(s) using a virtual particle Monte Carlo (“VPMC”) dose calculation engine that utilizes virtual particles. Virtual particles inherit the physical properties from realistic particles, but are conceptually designed for parallel computing in graphics processing units (“GPUs”) by avoiding the simulation of secondary particles. Simulation of virtual particles instead of realistic particles takes full advantage of the GPU hardware architecture (e.g., by avoiding thread divergence and racing conditions).

System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.