Patents by Inventor Mikko Hakala
Mikko Hakala 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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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: 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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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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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
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Patent number: 11612761Abstract: 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: GrantFiled: December 16, 2020Date of Patent: March 28, 2023Assignee: VARIAN MEDICAL SYSTEMS INTERNATIONAL AGInventors: Shahab Basiri, Mikko Hakala, Esa Kuusela, Elena Czeizler
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Publication number: 20230087944Abstract: Methods and systems for configuring a plan optimizer model for radiotherapy treatment is presented herein in which a processor iteratively trains a machine learning model configured to predict a heuristic parameter, wherein with each iteration, an agent of the machine learning model identifies a test heuristic parameter; transmits the test heuristic parameter to the plan optimizer model configured to receive one or more radiotherapy treatment attributes and predict a treatment plan; and identifies a reward for the test heuristic parameter based on execution performance value of the plan optimizer model, wherein the processor iteratively trains a policy of the machine learning model until the policy satisfies an accuracy threshold based on maximizing the reward.Type: ApplicationFiled: September 20, 2021Publication date: March 23, 2023Inventors: Mikko HAKALA, Elena CZEIZLER, Shahab BASIRI, Esa KUUSELA
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Patent number: 11590367Abstract: 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 16, 2020Date of Patent: February 28, 2023Assignee: VARIAN MEDICAL SYSTEMS INTERNATIONAL AGInventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Publication number: 20220415472Abstract: 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: ApplicationFiled: June 28, 2021Publication date: December 29, 2022Applicant: Varian Medical Systems, Inc.Inventors: Mikko Hakala, Esa Kuusela, Elena Czeizler, Shahab Basiri
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Publication number: 20220305285Abstract: 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: ApplicationFiled: March 25, 2021Publication date: September 29, 2022Inventors: Elena Czeizler, Esa Kuusela, Mikko Hakala, Shahab Basiri
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Patent number: 11395929Abstract: These teachings serve to facilitate radiating a treatment target in a patient during a radiation treatment session with a radiation treatment platform having a moving source of radiation and using an optimized radiation treatment plan. These teachings in particular provide for configuring the radiation treatment platform in a half-fan trajectory arrangement. These teachings then provide for beginning the radiation treatment session with the source of radiation in a first location and an isocenter for the treatment target in a first position. Then, during the radiation treatment session, these teachings provide for moving the source of radiation from that first location in synchronization with moving the isocenter from the aforementioned first position.Type: GrantFiled: September 24, 2020Date of Patent: July 26, 2022Assignee: Varian Medical Systems International AGInventors: Esa Kuusela, Mikko Hakala, Shahab Basiri, Elena Czeizler
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Publication number: 20220184419Abstract: 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: December 16, 2020Publication date: June 16, 2022Inventors: Shahab BASIRI, Mikko HAKALA, Esa KUUSELA, Elena CZEIZLER
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Publication number: 20220184421Abstract: 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 16, 2020Publication date: June 16, 2022Inventors: Mikko HAKALA, Esa KUUSELA, Elena CZEIZLER, Shahab BASIRI
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Publication number: 20220093242Abstract: 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: ApplicationFiled: September 23, 2020Publication date: March 24, 2022Inventors: Mikko HAKALA, Esa KUUSELA, Elena CZEIZLER, Shahab BASIRI