Patents by Inventor Maria JOHNSSON
Maria JOHNSSON 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: 11955234Abstract: Graphical user interfaces for use with extracorporeal blood treatment systems may include a plurality of mini settings cards corresponding to a plurality of settings cards. The mini settings cards may display one or more user-interactable settings, and may be selected to display the corresponding settings card. Further, each settings card may be accessed in other ways to selection of mini settings card such as, for example, by selection of a process feature graphical element corresponding the settings card.Type: GrantFiled: June 27, 2017Date of Patent: April 9, 2024Assignee: Gambro Lundia ABInventors: Bendik Torvin, Par-Olof Hakansson, Maria Johnsson, AnnMargret Hakansson, Roger Nilsson
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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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Publication number: 20230420112Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.Type: ApplicationFiled: June 14, 2023Publication date: December 28, 2023Inventors: Johan Martin Brynolfsson, Kerstin Elsa Maria Johnsson, Hannicka Maria Eleonora Sahlstedt
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Publication number: 20230351586Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.Type: ApplicationFiled: July 2, 2021Publication date: November 2, 2023Inventors: Johan Martin Brynolfsson, Kerstin Elsa Maria Johnsson, Hannicka Maria Eleonora Sahlstedt, Jens Filip Andreas Richter
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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: 11721428Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.Type: GrantFiled: August 31, 2020Date of Patent: August 8, 2023Assignee: EXINI Diagnostics ABInventors: Johan Martin Brynolfsson, Kerstin Elsa Maria Johnsson, Hannicka Maria Eleonora Sahlstedt
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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: 11430560Abstract: Graphical user interfaces for use with extracorporeal blood treatment systems may include a plurality of displayable settings cards. The plurality of settings cards may be grouped or arranged into a card set and a plurality of card subsets. Each of the card set and the plurality of card subsets may be displayable as a stack of settings cards with at least one settings card presented at the forefront of the stack to a user. Further, each settings card of the plurality of settings cards may also be displayed by itself.Type: GrantFiled: June 27, 2017Date of Patent: August 30, 2022Assignee: Gambro Lundia ABInventors: Annmargret Hakansson, Bendik Torvin, Maria Johnsson, Par-Olof Hakansson, Roger Nilsson
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Patent number: 11386988Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).Type: GrantFiled: September 14, 2020Date of Patent: July 12, 2022Assignee: EXINI Diagnostics ABInventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Patent number: 11369320Abstract: Graphical user interfaces for use with extracorporeal blood treatment systems may include a human-shaped graphical element and one or more process feature graphical elements. The human-shaped graphical element may be moved automatically or manually by users with respect to the process feature graphical elements to provide indications with respect to the process features corresponding to the human-shaped graphical element and the process feature graphical elements.Type: GrantFiled: June 30, 2016Date of Patent: June 28, 2022Assignee: Gambro Lundia ABInventors: Annmargret Håkansson, Par-Olof Håkansson, Maria Johnsson, Roger Nilsson, Bendik Torvin
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Patent number: 11321844Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).Type: GrantFiled: August 31, 2020Date of Patent: May 3, 2022Assignee: EXINI Diagnostics ABInventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Publication number: 20220005586Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.Type: ApplicationFiled: August 31, 2020Publication date: January 6, 2022Inventors: Johan Martin Brynolfsson, Kerstin Elsa Maria Johnsson, Hannicka Maria Eleonora Sahlstedt
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Publication number: 20210335480Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).Type: ApplicationFiled: September 14, 2020Publication date: October 28, 2021Inventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Publication number: 20210334974Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).Type: ApplicationFiled: August 31, 2020Publication date: October 28, 2021Inventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Patent number: 10973486Abstract: 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 organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure 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: June 7, 2018Date of Patent: April 13, 2021Assignees: Progenics Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson
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Publication number: 20200342600Abstract: 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 organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure 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 7, 2019Publication date: October 29, 2020Inventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson
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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
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Publication number: 20190267138Abstract: Graphical user interfaces for use with extracorporeal blood treatment systems may include a plurality of mini settings cards corresponding to a plurality of settings cards. The mini settings cards may display one or more user-interactable settings, and may be selected to display the corresponding settings card. Further, each settings card may be accessed in other ways to selection of mini settings card such as, for example, by selection of a process feature graphical element corresponding the settings card.Type: ApplicationFiled: June 27, 2017Publication date: August 29, 2019Applicant: Gambro Lundia ABInventors: Bendik TORVIN, Par-Olof HÅKANSSON, Maria JOHNSSON, AnnMargret HÅKANSSON, Roger NILSSON
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Publication number: 20190209116Abstract: 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 organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure 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: June 7, 2018Publication date: July 11, 2019Inventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson
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Publication number: 20190198152Abstract: Graphical user interfaces for use with extracorporeal blood treatment systems may include a plurality of displayable settings cards. The plurality of setting cards may be grouped or arranged into a card set and a plurality of card subsets. Each of the card set and the plurality of card subsets may be displayable as a stack of settings cards with at least one settings card presented at the forefront of the stack to a user. Further, each settings card of the plurality of settings cards may also be displayed by itself.Type: ApplicationFiled: June 27, 2017Publication date: June 27, 2019Inventors: Annmargret Hakansson, Bendik Torvin, Maria Johnsson, Par-Olof Hakansson, Roger Nilsson