Patents by Inventor Aseem Undvall Anand
Aseem Undvall Anand 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: 20240127437Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.Type: ApplicationFiled: December 28, 2023Publication date: April 18, 2024Inventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Patent number: 11941817Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.Type: GrantFiled: March 29, 2023Date of Patent: March 26, 2024Assignee: EXINI Diagnostics ABInventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand
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Patent number: 11900597Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.Type: GrantFiled: August 21, 2020Date of Patent: February 13, 2024Assignees: Progenics Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Publication number: 20230410985Abstract: Presented herein are systems and methods that provide semi-automated and/or automated analysis of medical image data to determine and/or convey values of metrics that provide a picture of a patient's risk and/or disease. Technologies described herein include systems and methods for analyzing medical image data to evaluate quantitative metrics that provide snapshots of patient disease burden at particular times and/or for analyzing images taken over time to produce a longitudinal dataset that provides a picture of how a patient's risk and/or disease evolves over time during surveillance and/or in response to treatment. Metrics computed via image analysis tools described herein may themselves be used as quantitative measures of disease burden and/or may be linked to clinical endpoints that seek to measure and/or stratify patient outcomes.Type: ApplicationFiled: June 8, 2023Publication date: December 21, 2023Inventors: Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt, Jens Filip Andreas Richter, Karl Vilhelm Sjöstrand, Aseem Undvall Anand
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Publication number: 20230316530Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.Type: ApplicationFiled: March 29, 2023Publication date: October 5, 2023Inventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand
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Patent number: 11657508Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.Type: GrantFiled: January 6, 2020Date of Patent: May 23, 2023Assignee: EXINI Diagnostics ABInventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand
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Patent number: 11564621Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.Type: GrantFiled: January 6, 2020Date of Patent: January 31, 2023Assignees: Progenies Pharmacenticals, Inc., EXINI Diagnostics ABInventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Publication number: 20220398724Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.Type: ApplicationFiled: August 21, 2020Publication date: December 15, 2022Inventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Publication number: 20210093249Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.Type: ApplicationFiled: January 6, 2020Publication date: April 1, 2021Inventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Publication number: 20200245960Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.Type: ApplicationFiled: January 6, 2020Publication date: August 6, 2020Inventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand