公开(公告)号：US20240363252A1

公开(公告)日：2024-10-31

申请号：US18560081

申请日：2022-05-12

申请人： The Jackson Laboratory

发明人： Vivek Kumar ,  Leinani Hession ,  Gautam Sabnis ,  Gary Churchill

IPC分类号： G16H50/30 ,  G06T7/00 ,  G16H30/40

CPC分类号： G16H50/30 ,  G06T7/0016 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30012

摘要： Systems and methods described herein provide techniques for determining a visual frailty score by processing video data of a subject. Various features maybe used to determine the visual frailty score, including but not limited to, spinal mobility features, gait measurements, behavior features, and body composition data. The various features may be extracted from the video data using different techniques. The various features may be processed using one or more machine learning models to determine the visual frailty score.

公开(公告)号：US20240363209A1

公开(公告)日：2024-10-31

申请号：US18770292

申请日：2024-07-11

申请人： Magic Leap, Inc.

发明人： Nastasja U. ROBAINA ,  Nicole Elizabeth SAMEC ,  Mark BAERENRODT ,  Christopher M. HARRISES

IPC分类号： G16H10/60 ,  A61B3/00 ,  A61B3/10 ,  A61B3/113 ,  A61B5/00 ,  A61B5/06 ,  A61B5/1171 ,  A61B5/339 ,  A61B17/00 ,  A61B34/20 ,  A61B90/00 ,  A61B90/50 ,  G02B27/00 ,  G02B27/01 ,  G06F16/22 ,  G06F16/23 ,  G06F21/32 ,  G06F21/62 ,  G06F40/205 ,  G06F40/289 ,  G06V20/20 ,  G10L15/26 ,  G16H30/40 ,  G16H40/67

CPC分类号： G16H10/60 ,  A61B3/0041 ,  A61B3/10 ,  A61B3/113 ,  A61B5/0077 ,  A61B5/06 ,  A61B5/1171 ,  A61B5/1176 ,  A61B5/339 ,  A61B5/4803 ,  A61B90/37 ,  G02B27/0093 ,  G02B27/0172 ,  G06F16/22 ,  G06F16/2379 ,  G06F21/32 ,  G06F40/205 ,  G06F40/289 ,  G06V20/20 ,  G10L15/26 ,  G16H40/67 ,  A61B2017/00207 ,  A61B2017/00216 ,  A61B2034/2048 ,  A61B2090/365 ,  A61B2090/372 ,  A61B2090/502 ,  G02B2027/0127 ,  G02B2027/0138 ,  G02B2027/014 ,  G02B2027/0141 ,  G06F21/6245 ,  G16H30/40

摘要： A wearable device can present virtual content to the wearer for many applications in a healthcare setting. The wearer may be a patient or a healthcare provider (HCP). Such applications can include, but are not limited to, access, display, and modification of patient medical records and sharing patient medical records among authorized HCPs.

公开(公告)号：US12131513B2

公开(公告)日：2024-10-29

申请号：US17392875

申请日：2021-08-03

申请人： FUJIFILM Corporation

发明人： Seiya Takenouchi

IPC分类号： G06V10/25 ,  G06F18/2431 ,  G06T7/00 ,  G06T11/00 ,  G06V10/44 ,  G06V10/82 ,  G09G5/373 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20

CPC分类号： G06V10/25 ,  G06F18/2431 ,  G06T7/0012 ,  G06T11/00 ,  G06V10/454 ,  G06V10/82 ,  G09G5/373 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10068 ,  G06T2207/30096 ,  G06V2201/03 ,  G09G2380/08

摘要： The medical image processing apparatus includes: an image acquiring unit that acquires a medical image; a classification unit that classifies, on the basis of the medical image acquired by the image acquiring unit, the medical image or a region of interest included in the medical image; a notification information generating unit that generates, in accordance with a classification result of the classification, first notification information for display and second notification information for storage, the second notification information differing from the first notification information; and a storage that stores the medical image and the second notification information.

公开(公告)号：US12131475B2

公开(公告)日：2024-10-29

申请号：US17191868

申请日：2021-03-04

申请人： UnitedHealth Group Incorporated

发明人： Russell H. Amundson ,  Saurabh Bhargava ,  Rama Krishna Singh ,  Ravi Pande ,  Vishwakant Gupta ,  Gaurav Mantri ,  Abhinav Agrawal ,  Sapeksh Suman

IPC分类号： G06T7/00 ,  G06F18/2137 ,  G06F18/24 ,  G06T3/60 ,  G06T7/11 ,  G06T7/33 ,  G06T7/70 ,  G06T7/90 ,  G06V10/764 ,  G06V10/77 ,  G16H30/40 ,  G16H40/67

CPC分类号： G06T7/0014 ,  G06F18/21375 ,  G06F18/24 ,  G06T3/60 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/337 ,  G06T7/70 ,  G06T7/90 ,  G06V10/764 ,  G06V10/7715 ,  G16H30/40 ,  G16H40/67 ,  G06T2207/10024 ,  G06T2207/20024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30012 ,  G06T2207/30068 ,  G06T2207/30088 ,  G06T2207/30096

摘要： Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.

公开(公告)号：US12127794B2

公开(公告)日：2024-10-29

申请号：US17597042

申请日：2020-06-25

申请人： Quantum Surgical

发明人： Estanislao Oubel ,  Lucien Blondel ,  Fernand Badano ,  Bertin Nahum

IPC分类号： A61B18/00 ,  A61B18/02 ,  A61B18/04 ,  A61B18/18 ,  A61B18/20 ,  A61B34/10 ,  G06N3/045 ,  G06N3/08 ,  G16H20/40 ,  G16H30/40 ,  G16H50/50 ,  G16H50/70

CPC分类号： A61B34/10 ,  A61B18/02 ,  A61B18/04 ,  A61B18/1815 ,  A61B18/20 ,  G06N3/045 ,  G06N3/08 ,  G16H20/40 ,  G16H30/40 ,  G16H50/50 ,  G16H50/70 ,  A61B2018/00577 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108

摘要： The invention relates to a method and device for planning a surgical procedure aiming to ablate a tissue in an anatomical area of interest of a patient. On the basis of a preoperative image of the anatomical area of interest and of a set of planning parameters (P), a simulated image is generated by a neural network that has been previously trained using learning elements corresponding, respectively, to a similar surgical procedure for ablating a tissue in an anatomical area of interest for another patient. Each learning element comprises a preoperative image of the anatomical area of interest of a patient, planning parameters (P) used for the surgical procedure on this patient, and a postoperative image of the anatomical area of interest of this patient after the surgical procedure. An estimated ablation region may be segmented in the simulated image in order to be compared with a segmented region to be treated in the preoperative image.

公开(公告)号：US12127793B2

公开(公告)日：2024-10-29

申请号：US17428193

申请日：2020-02-04

申请人： Smith & Nephew, Inc. ,  Smith & Nephew Asia Pacific Pte. Limited ,  Smith & Nephew Orthopaedics AG

发明人： Shawn P. McGuan ,  Elizabeth Duxbury

IPC分类号： A61B34/10 ,  A61B17/17 ,  A61B34/00 ,  A61B90/00 ,  A61F2/46 ,  G02B27/01 ,  G06N3/08 ,  G06N5/046 ,  G06N20/00 ,  G06N20/10 ,  G09B5/02 ,  G09B19/00 ,  G09B19/24 ,  G16H10/60 ,  G16H20/40 ,  G16H30/40 ,  G16H40/63 ,  G16H50/50 ,  G16H50/70 ,  A61B17/00 ,  A61B17/15 ,  A61B34/20 ,  A61B34/30 ,  A61B90/50 ,  A61B90/96 ,  A61F2/30

CPC分类号： A61B34/10 ,  A61B17/1764 ,  A61B34/25 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61F2/461 ,  G02B27/0172 ,  G06N3/08 ,  G06N5/046 ,  G06N20/00 ,  G06N20/10 ,  G09B5/02 ,  G09B19/003 ,  G09B19/24 ,  G16H10/60 ,  G16H20/40 ,  G16H30/40 ,  G16H40/63 ,  G16H50/50 ,  G16H50/70 ,  A61B2017/00199 ,  A61B2017/00526 ,  A61B17/15 ,  A61B17/17 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B34/20 ,  A61B2034/2048 ,  A61B2034/2057 ,  A61B2034/2063 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/2072 ,  A61B2034/2074 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2034/256 ,  A61B2034/258 ,  A61B34/30 ,  A61B2090/363 ,  A61B2090/365 ,  A61B2090/371 ,  A61B2090/376 ,  A61B2090/3916 ,  A61B2090/502 ,  A61B90/96 ,  A61F2002/30952 ,  A61F2002/4633 ,  G02B2027/0138

摘要： A method and system for performing hip arthroplasty include analyzing images of a patient's hip joint in a plurality of positions to identify preoperative hip geometry. A statistical patient model predicts prosthetic hip implant performance based on the preoperative knee geometry and given prosthetic knee implant implantation parameters for a plurality of selected patient activities, each having a predefined motion profile to calculate an optimized surgical plan for performing the procedure using a computer assisted surgical system, which may use fiducial markers affixed to patient tissue. Hip geometry can be determined by angles between landmarks in the images, including sacral tilt, pelvic incidence, pelvic femoral angle, and ante-inclination angle in x-ray images. Implant performance criteria can include, for example, edge loading and range of motion of implant components.

公开(公告)号：US20240355454A1

公开(公告)日：2024-10-24

申请号：US18761796

申请日：2024-07-02

申请人： KONINKLIJKE PHILIPS N.V.

发明人： Eberhard Sebastian Hansis ,  Falk Uhlemann ,  Thomas Netsch ,  Jörn Borgert ,  Michael Günter Helle

IPC分类号： G16H30/20 ,  G16H10/60 ,  G16H30/40 ,  G16H40/20

CPC分类号： G16H30/20 ,  G16H10/60 ,  G16H30/40 ,  G16H40/20

摘要： To obtain feedback on image quality from qualified reviewers, an optically machine readable code (e.g., a QR code or the like) is generated for each acquired medical image and embedded into the image. The embedded code includes information to the identity of the image, the imaging device, authorized reviewers, and authorized recipients of the feedback, as well as a link to a feedback form that can be retrieved by a communication device used by an authorized user. When the embedded code is scanned by the communication device, the code is decoded and the feedback form is retrieved from a server, completed by the reviewer, and transmitted back to the authorized recipients of the feedback.

公开(公告)号：US12121747B2

公开(公告)日：2024-10-22

申请号：US17208781

申请日：2021-03-22

申请人： Siemens Healthineers International AG

发明人： Jarkko Peltola ,  Marko Rusanen ,  Ville Pietila

IPC分类号： G16H20/40 ,  A61N5/10 ,  G06N20/00 ,  G16H30/40

CPC分类号： A61N5/1031 ,  A61N5/1038 ,  G06N20/00 ,  G16H20/40 ,  G16H30/40

摘要： Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

公开(公告)号：US20240347172A1

公开(公告)日：2024-10-17

申请号：US18301697

申请日：2023-04-17

申请人： Optum, Inc.

发明人： Sara Daneshvar ,  Paul Alain Vial ,  David Dubois

IPC分类号： G16H30/40

CPC分类号： G16H30/40

摘要： In order to improve medical care, systems and methods for identifying relevant studies are provided. An A.I. model, such as a machine learning model, is trained to identify findings associated with studies based on the text of reports included in the studies. Later, when a medical professional is viewing a current study for a patient in a PACS viewer or application, the model is used to determine findings associated with previous studies associated with the patient. These determined findings are displayed to the medical professional in the PACS viewer. The medical professional may then select a finding that she may think is relevant to the current study. In response, one or more previous studies associated with the selected finding may be displayed to the medical professional in the PACS viewer including portions of the associated reports or images from the previous studies.

公开(公告)号：US20240347171A1

公开(公告)日：2024-10-17

申请号：US18301624

申请日：2023-04-17

申请人： Optum, Inc.

发明人： David Dubois ,  Sara Daneshvar ,  Paul Alain Vial ,  Jaime Lea Ekis

IPC分类号： G16H30/40 ,  G06T7/00 ,  G16H30/20

CPC分类号： G16H30/40 ,  G06T7/0014 ,  G16H30/20 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30168

摘要： Systems, methods, and apparatuses implementing a display optimization system are provided herein. In some embodiments, an example display optimization system may be configured to perform an optimal anchor-prior matching operation to identify optimal anchor-prior image pairs or series pairs from new medical imaging data (e.g., one or more anchor image series) and historical medical imaging data (e.g., one or more prior image series).