Patents by Inventor Elena CZEIZLER
Elena CZEIZLER has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12138476Abstract: A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.Type: GrantFiled: September 27, 2021Date of Patent: November 12, 2024Assignee: Siemens Healthineers International AGInventors: Elena Czeizler, Mikko Hakala, Shahab Basiri, Hannu Laaksonen, Maria Cordero Marcos, Christopher Boylan, Jarkko Peltola, Ville Pietila, Esa Kuusela
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Patent number: 12138483Abstract: A control circuit accesses patient information including anatomical image information of the patient, segmentation information corresponding to the anatomical image information, and a dose map for the radiation treatment plan. The control circuit then generates at least one organ-specific three-dimensional risk map as a function of the patient information and presents that risk map to a user via a display.Type: GrantFiled: March 31, 2020Date of Patent: November 12, 2024Assignee: Siemens Healthineers International AGInventors: Elena Czeizler, Esa Kuusela, Maria Isabel Cordero Marcos, Hannu Laaksonen, Jan Schreier
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Patent number: 12109434Abstract: 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.Type: GrantFiled: December 1, 2023Date of Patent: October 8, 2024Assignee: Siemens Healthineers International AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Patent number: 12080402Abstract: Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.Type: GrantFiled: June 28, 2021Date of Patent: September 3, 2024Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Patent number: 12068065Abstract: Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.Type: GrantFiled: April 27, 2023Date of Patent: August 20, 2024Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Esa Heikki Kuusela, Shahab Basiri, Elena Czeizler, Mikko Oskari Hakala, Lauri Jaakonpoika Halko
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Publication number: 20240207644Abstract: Provided herein are methods and systems to train and execute a model that uses artificial intelligence methodologies to learn and predict dosages administrated to different structures during radiotherapy treatment. A method comprises receiving a value indicating a prioritization between a first organ at risk of a patient and a second organ at risk of the patient receiving radiation dosage; executing an artificial intelligence model using the value to predict a radiation dosage for the first organ at risk, the second organ at risk, and a target structure, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, dosage administered to the participant's target structure, first organ at risk, and second organ at risk; and outputting the predicted radiation dosage for at least one of the first organ at risk, the second organ at risk, and a target structure.Type: ApplicationFiled: December 21, 2022Publication date: June 27, 2024Applicant: Siemens Healthineers International AGInventors: Simeng Zhu, Maria Isabel Cordero Marcos, Elena Czeizler, Supratik Bose, Anthony Magliari
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Publication number: 20240115883Abstract: 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.Type: ApplicationFiled: December 1, 2023Publication date: April 11, 2024Applicant: Siemens Healthineers International AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Patent number: 11931598Abstract: Image information regarding a particular patient is provided, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing non-image clinical information that corresponds to the particular patient. A control circuit accesses the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.Type: GrantFiled: March 25, 2021Date of Patent: March 19, 2024Assignee: Varian Medical Systems International AGInventors: Elena Czeizler, Esa Kuusela, Mikko Hakala, Shahab Basiri
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Publication number: 20240079113Abstract: Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.Type: ApplicationFiled: April 27, 2023Publication date: March 7, 2024Inventors: Esa Heikki KUUSELA, Shahab BASIRI, Elena CZEIZLER, Mikko Oskari HAKALA, Lauri Jaakonpoika HALKO
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Publication number: 20240042238Abstract: A control circuit accesses patient image content as well as field geometry information regarding a particular radiation treatment platform. The control circuit then generates a predicted three-dimensional dose map for the radiation treatment plan as a function of both the patient image content and the field geometry information.Type: ApplicationFiled: October 4, 2023Publication date: February 8, 2024Inventors: Elena Czeizler, Esa Kuusela, Mikko Hakala, Shahab Basiri
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Patent number: 11865369Abstract: 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.Type: GrantFiled: February 27, 2023Date of Patent: January 9, 2024Assignee: Siemens Healthineers International AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Publication number: 20240001139Abstract: A control circuit accesses a plurality of information items that each correspond to a resultant dose volume histogram shape for a corresponding different radiation treatment plan. The control circuit then trains a machine learning model to predict a desired dose volume histogram shape using that plurality of information items as a training corpus.Type: ApplicationFiled: June 30, 2022Publication date: January 4, 2024Inventors: Esa Kuusela, Mikko Hakala, María Isabel Cordero-Marcos, Elena Czeizler, Shahab Basiri, Hannu Laaksonen
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Publication number: 20240001138Abstract: A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).Type: ApplicationFiled: June 29, 2022Publication date: January 4, 2024Inventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri, María Isabel Cordero-Marcos, Hannu Laaksonen, Alexander E. Maslowski
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Patent number: 11813479Abstract: A control circuit accesses patient image content as well as field geometry information regarding a particular radiation treatment platform. The control circuit then generates a predicted three-dimensional dose map for the radiation treatment plan as a function of both the patient image content and the field geometry information.Type: GrantFiled: June 11, 2020Date of Patent: November 14, 2023Assignee: Siemens Healthineers International AGInventors: Elena Czeizler, Esa Kuusela, Mikko Hakala, Shahab Basiri
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Publication number: 20230347175Abstract: Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward.Type: ApplicationFiled: March 27, 2023Publication date: November 2, 2023Applicant: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Shahab Basiri, Mikko Hakala, Esa Kuusela, Elena Czeizler
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Publication number: 20230307113Abstract: A control circuit accesses a plurality of previously-optimized radiation treatment plans and also accesses a plurality of optimization precursor information items. Each of the latter corresponds to at least one of the plurality of previously-optimized radiation treatment plans. The control circuit then generates a machine learning model using the plurality of previously-optimized radiation treatment plans and the plurality of optimization precursor information items. By one approach, at least a majority of the plurality of optimization precursor information items originate with a given radiation treatment facility and not with an unrelated (physically or institutionally) facility. These teachings will accommodate use of any of a variety of optimization precursor information items. By one approach, at least some of the plurality of optimization precursor information items comprise clinical goals.Type: ApplicationFiled: March 25, 2022Publication date: September 28, 2023Inventors: Esa Kuusela, Elena Czeizler
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Patent number: 11710558Abstract: Disclosed herein are systems and methods for identifying radiation therapy treatment data for different patients, such as field geometry. A central server collects patient data, radiation therapy treatment planning data, clinic-specific rules, and other pertinent treatment/medical data associated with a patient. The server then executes one or more machine-learning computer models to predict field geometry variables and weights associated with the patient's treatments. Using the predicted variables and weights, the server execute a clinic-specific set of logic to identify suggested field geometry, such as couch/gantry angles and/or arc attributes. The server then monitors whether end users (e.g., medical professionals) revise the suggested field geometry and trains the model accordingly.Type: GrantFiled: September 23, 2020Date of Patent: July 25, 2023Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Publication number: 20230211184Abstract: 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.Type: ApplicationFiled: February 27, 2023Publication date: July 6, 2023Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AGInventors: Mikko HAKALA, Esa KUUSELA, Elena CZEIZLER, Shahab BASIRI
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Patent number: 11651848Abstract: Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.Type: GrantFiled: March 27, 2020Date of Patent: May 16, 2023Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Esa Heikki Kuusela, Shahab Basiri, Elena Czeizler, Mikko Oskari Hakala, Lauri Jaakonpoika Halko
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Publication number: 20230095485Abstract: A radiation treatment plan three-dimensional dose prediction machine learning model is trained using a training corpus that includes a plurality of radiation treatment plans that are not specific to a particular patient and wherein the training corpus includes some, but not all, possible patient volumes of interest. Information regarding the patient (including information regarding at least one volume of interest for the patient that was not represented in the training corpus) is input to the radiation treatment plan three-dimensional dose prediction machine model. The latter generates predicted three-dimensional dose distributions that include a predicted three-dimensional dose distribution for the at least one volume of interest that was not represented in the training corpus.Type: ApplicationFiled: September 27, 2021Publication date: March 30, 2023Inventors: Elena Czeizler, Mikko Hakala, Shahab Basiri, Hannu Laaksonen, Maria Isabel Cordero Marcos, Christopher Boylan, Jarkko Y. Peltola, Ville Pietilä, Esa Kuusela