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公开(公告)号：US20230410985A1
公开(公告)日：2023-12-21
申请号：US18207246
申请日：2023-06-08
Applicant: EXINI Diagnostics AB , Progenics Pharmaceuticals, Inc.
Inventor： Johan Martin Brynolfsson , Hannicka Maria Eleonora Sahlstedt , Jens Filip Andreas Richter , Karl Vilhelm Sjöstrand , Aseem Undvall Anand
CPC classification number: G16H30/40 , G16H50/30 , G06T7/11 , G06T2207/10028 , G06T2207/30096
Abstract: 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. Accordingly, image analysis technologies of the present disclosure may be used to inform clinical decision making, evaluate of treatment efficacy, and predict patient response(s).
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公开(公告)号：US20250061580A1
公开(公告)日：2025-02-20
申请号：US18934154
申请日：2024-10-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/11 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: 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.
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3.发明申请公开(公告)号：US20220398724A1
公开(公告)日：2022-12-15
申请号：US17762796
申请日：2020-08-21
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Abstract: 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.
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4.发明申请公开(公告)号：US20210093249A1
公开(公告)日：2021-04-01
申请号：US16734609
申请日：2020-01-06
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Abstract: 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.
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公开(公告)号：US12243236B1
公开(公告)日：2025-03-04
申请号：US18934154
申请日：2024-10-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/00 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06T7/11 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20250069232A1
公开(公告)日：2025-02-27
申请号：US18934158
申请日：2024-10-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/11 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: 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.
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7.发明公开公开(公告)号：US20240285248A1
公开(公告)日：2024-08-29
申请号：US18530831
申请日：2023-12-06
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Inventor： Karl Vilhelm Sjöstrand , Aseem Undvall Anand
CPC classification number: A61B6/5217 , A61B6/032 , A61B6/037 , A61B6/50 , A61B6/5235 , A61B6/563 , G06T7/0012 , G06T7/11 , G16H50/30 , G06T2207/10081 , G06T2207/10104 , G06T2207/20081 , G06T2207/20084 , G06T2207/30081 , G06T2207/30096
Abstract: Presented herein are systems and methods for predicting biochemical progression free survival (bPFS) in prostate cancer patients. In certain embodiments, bPFS is predicted from 18F-DCFPyL PET/CT images using deep learning (or other machine learning or artificial intelligence techniques) to segment anatomical information from the CT image and use this information in combination with the PET image to detect and quantify candidates for prostate cancer lesions.
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公开(公告)号：US20240169546A1
公开(公告)日：2024-05-23
申请号：US18429322
申请日：2024-01-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/11 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
CPC classification number: G06T7/11 , A61B6/032 , A61B6/037 , A61B6/463 , A61B6/466 , A61B6/481 , A61B6/505 , A61B6/507 , A61B6/5205 , A61B6/5241 , A61B6/5247 , A61K51/0455 , G06F18/214 , G06V20/64 , G06V20/695 , G06V20/698 , G06V30/2504 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50 , G06V2201/031 , G06V2201/033
Abstract: 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.
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9.发明公开公开(公告)号：US20240127437A1
公开(公告)日：2024-04-18
申请号：US18398846
申请日：2023-12-28
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
CPC classification number: G06T7/0012 , G06V10/25 , G16H30/40 , G16H50/20 , G06T2207/10081
Abstract: 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.
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公开(公告)号：US20200245960A1
公开(公告)日：2020-08-06
申请号：US16734599
申请日：2020-01-06
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: A61B6/00 , A61B6/03 , A61K51/04 , G06K9/00 , G06K9/68 , G06K9/62 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G16H50/30
Abstract: 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.
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