公开(公告)号：US20240226544A1

公开(公告)日：2024-07-11

申请号：US18559049

申请日：2022-05-05

申请人： Vektor Medical, Inc.

发明人： Robert Joseph Krummen

IPC分类号： A61N1/05 ,  A61N1/368 ,  G06N20/10 ,  G16H10/60 ,  G16H20/40 ,  G16H40/63 ,  G16H50/70

CPC分类号： A61N1/0587 ,  A61N1/3684 ,  G06N20/10 ,  G16H10/60 ,  G16H20/40 ,  G16H40/63 ,  G16H50/70

摘要： A system for guiding implantation of an energy delivery component of a cardiac pacing device at a fixation location within a heart of a patient is provided. During a procedure to implant an energy pulse delivery component, the system receives a patient cardiogram collected during pacing of the energy pulse delivery component while the energy pulse delivery component is positioned at a current location within the heart. The system then determines based on the patient cardiogram the current location of the energy pulse delivery component. The system then outputs an indication of the current location to guide affixing of the energy pulse delivery component at the intended fixation location. This process is repeated until the energy pulse delivery component is at the fixation location. The system also evaluates the effectiveness of pacing at intermediate location to optimize the final location based upon simulated electro-mechanics of the system in near-real time.

公开(公告)号：US20240225535A1

公开(公告)日：2024-07-11

申请号：US18511003

申请日：2023-11-16

申请人： ABBOTT DIABETES CARE INC.

发明人： Matthew T. Novak ,  Aparajita Bhattacharya ,  Gary A. Hayter

IPC分类号： A61B5/00 ,  A61B5/145 ,  G16H50/70 ,  G16H70/40

CPC分类号： A61B5/4839 ,  A61B5/0022 ,  A61B5/14532 ,  G16H50/70 ,  G16H70/40

摘要： Systems, devices and methods are provided for titration of a medication dose for a patient or user. The titration may be based on determining a hypoglycemic risk of a user for a plurality of time-of-day periods. The determination of the hypoglycemic risk may be accomplished by known methods, including the glucose pattern analysis, glucose dysregulation analysis, low alarm frequency analysis, and combinations thereof. The system may then select a recommended action based on the analyte pattern type. The system may then store an indicator of the recommended action in a computer memory for output. The system may then output the recommended action. The recommended action may be outputted to the user, an HCP, or a caregiver. The recommended action may also vary depending on who is receiving the recommended action.

公开(公告)号：US20240225513A1

公开(公告)日：2024-07-11

申请号：US18535007

申请日：2023-12-11

申请人： Frederick Scott Starr

发明人： Frederick Scott Starr

IPC分类号： A61B5/316 ,  A61B5/00 ,  A61B5/16 ,  A61B5/369 ,  G06F16/22 ,  G16H10/60 ,  G16H40/63 ,  G16H50/20 ,  G16H50/70

CPC分类号： A61B5/316 ,  A61B5/369 ,  A61B5/7275 ,  G16H40/63 ,  G16H50/70 ,  A61B5/16 ,  A61B5/6814 ,  A61B5/7257 ,  A61B5/726 ,  A61B2562/046 ,  G06F16/22 ,  G16H10/60 ,  G16H50/20

摘要： A computer implemented method for analyzing electroencephalogram signals can include a plurality of sensors configured to contact a skull and capture the electroencephalogram signals, one or more computer memory units for storing computer instructions and data, and one or more processors configured to perform the operations of clustering the electroencephalogram signals using at least stored objective data and added subjective data including patient profile data to provide clustered data results and predicting one or more among a medical diagnosis, assessment, plan, necessary forms, or recommendations for follow up based on the clustered data results.

公开(公告)号：US12033104B2

公开(公告)日：2024-07-09

申请号：US17643248

申请日：2021-12-08

申请人： Theator Inc.

发明人： Tamir Wolf ,  Dotan Asselmann ,  Evgeny Makrinich ,  Asaf Rafaeli ,  Barak Ariel

IPC分类号： A61B5/00 ,  A61B34/00 ,  A61B34/20 ,  A61B90/00 ,  G06N20/00 ,  G06Q10/0639 ,  G06T7/00 ,  G06T7/20 ,  G06T7/70 ,  G06T7/90 ,  G06V10/764 ,  G06V20/40 ,  G11B27/10 ,  G11B27/11 ,  G11B27/34 ,  G16H10/60 ,  G16H15/00 ,  G16H20/40 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/30 ,  G16H50/70 ,  G16H70/20 ,  G06F3/0482 ,  G16H50/20

CPC分类号： G06Q10/06398 ,  A61B34/20 ,  A61B34/25 ,  A61B90/37 ,  G06N20/00 ,  G06T7/0014 ,  G06T7/20 ,  G06T7/70 ,  G06T7/90 ,  G06V10/764 ,  G06V20/40 ,  G06V20/41 ,  G06V20/46 ,  G06V20/48 ,  G06V20/49 ,  G11B27/102 ,  G11B27/11 ,  G11B27/34 ,  G16H10/60 ,  G16H15/00 ,  G16H20/40 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/30 ,  G16H50/70 ,  G16H70/20 ,  A61B2034/2065 ,  A61B2034/256 ,  A61B2560/0487 ,  G06F3/0482 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V20/44 ,  G06V2201/03 ,  G06V2201/034 ,  G16H50/20

摘要： Systems, methods, and computer readable media related to aggregating and associated captured medical data are disclosed. They involve receiving an ID of a piece of equipment in a medical facility, location information for the equipment, and medical information captured by the equipment; and ascertaining a time of information capture by the equipment. They further involve performing a lookup in a data record to determine an identity of a particular patient assigned to a location associated with the location information and performing a lookup in a data structure to identify a medical record of the particular patient. They further involve establishing an association between the medical information captured by the equipment and the medical record to thereby enable access to the medical information through access to the medical record of the particular patient.

公开(公告)号：US20240221959A1

公开(公告)日：2024-07-04

申请号：US18288612

申请日：2022-04-28

申请人： UCB BIOPHARMA SRL

发明人： Karim MALKI ,  Llenalia Maria GERCIA FERNANDEZ

IPC分类号： G16H50/50 ,  G06N20/20 ,  G16H30/40 ,  G16H50/70

CPC分类号： G16H50/50 ,  G06N20/20 ,  G16H50/70 ,  G16H30/40

摘要： A method is provided for constructing a machine learning algorithm for predicting a response variable for a neurological condition. The method includes extracting variables for characterizing a patient cohort from electronic historical medical data; selecting variables from the extracted variables using a random forest defined by an initial response variable; fitting a Bayesian generalized linear mixed model (GLMM) using the initial response variable; extracting a predictive probability from the Bayesian GLMM for each of the selected variables; determining a target response variable from the predictive probability and the initial response variable; using the random forest and the Bayesian GLMM to obtain final estimated predictive probabilities based on target response variable for each of the selected variables to identify relevant selected variables; and constructing the machine learning algorithm to utilize the relevant selected variables for predicting the response variable for the neurological condition.

公开(公告)号：US20240216150A1

公开(公告)日：2024-07-04

申请号：US18601922

申请日：2024-03-11

申请人： Russell J. Bodner

发明人： Russell J. Bodner

IPC分类号： A61F2/46 ,  A61B34/10 ,  A61B34/20 ,  G16H20/40 ,  G16H40/20 ,  G16H40/40 ,  G16H40/63 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60

CPC分类号： A61F2/4609 ,  A61B34/10 ,  A61F2/4607 ,  G16H20/40 ,  G16H40/20 ,  G16H40/40 ,  G16H40/63 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  A61B2034/108 ,  A61B34/20 ,  A61F2002/4632 ,  A61F2002/4633 ,  A61F2002/4668 ,  A61F2002/4688 ,  A61F2002/4689 ,  G05B2219/45119

摘要： The disclosure provides example methods and non-transitory computer-readable mediums for acetabular cup placement. An example method includes a processor (a) determining for a first patient a sagittal acetabular cup position in the form of a standing AI, a seated AI and a SAA based on (i) a standing SS relative to a normative SS, (ii) a dSS between a standing position and an upright seated position, (iii) a femoral version corresponding to a femoral version outlier position, and (iv) a PFA to correspond to a PFA outlier position in a standing position or an upright seated position, (b) determining a coronal acetabular cup position in the form of a supine coronal anteversion and at least one of a supine or a standing coronal inclination based on the sagittal acetabular cup position, and (c) determining a post-operative standing AI and a post-operative seated AI based on the coronal acetabular cup position.

公开(公告)号：US12027271B2

公开(公告)日：2024-07-02

申请号：US17051002

申请日：2019-04-30

申请人： HOFFMANN-LA ROCHE INC.

发明人： Thomas Schoedl ,  Stefanie Kaufmann ,  Markus Bundschus ,  Antonia Stank ,  Marius Rene Garmhausen

IPC分类号： G16H50/30 ,  G16H50/70

CPC分类号： G16H50/30 ,  G16H50/70

摘要： The mobile device receives a prediction result via a digital cellular mobile telecommunication network. The prediction result has been generated by a program logic using a biomedical model. The prediction result includes a prediction score, a first confidence interval of prediction scores known to have a percentage of false negative predictions below a predefined FN-percentage threshold and a second confidence interval of score values known to have a percentage of false positive predictions below a predefined FP-percentage threshold. The mobile device displays an analog scale icon including a background area having the prediction score, an analog scale representing the score range; a pointer; a first sub-range indicator indicating the size and position of the first confidence interval; and a second sub-range indicator indicating the size and position of the second confidence interval within the score range.

公开(公告)号：US12027250B2

公开(公告)日：2024-07-02

申请号：US16768081

申请日：2018-10-02

申请人： Sony Olympus Medical Solutions Inc.

发明人： Mitsuaki Shiraga

IPC分类号： G16H20/40 ,  A61B1/00 ,  A61B5/02 ,  A61B90/00 ,  A61M16/01 ,  B25J9/16 ,  G05B13/02 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/70 ,  G16H70/20

CPC分类号： G16H20/40 ,  A61B1/00009 ,  A61B5/02042 ,  A61B90/37 ,  A61M16/01 ,  B25J9/1697 ,  G05B13/0265 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/70 ,  G16H70/20 ,  G05B2219/45117

摘要： The present disclosure provides a medical information processing apparatus that comprises: a selection unit that selects, from among candidate data including surgery data acquired during surgery, target data corresponding to selection conditions that include a condition relating to a patient attribute, a condition relating to a surgical-procedure type, and a condition relating to a disease type; an extraction unit that detects a feature from the selected target data and extracts, from the target data, feature data corresponding to the detected feature; and an editing processing unit that edits the extracted feature data, wherein the extraction unit extracts at least a medical image as the feature data.

公开(公告)号：US12025615B2

公开(公告)日：2024-07-02

申请号：US16647434

申请日：2018-09-13

申请人： Arizona Board of Regents on behalf of Arizona State University

发明人： Stephen Johnston

IPC分类号： G01N31/00 ,  C07K16/28 ,  G01N33/53 ,  G01N33/543 ,  G01N33/574 ,  G16B40/00 ,  G16H10/40 ,  G16H20/00 ,  G16H50/00 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  C07K14/705 ,  G16H10/60

CPC分类号： G01N33/574 ,  C07K16/2818 ,  C07K16/2827 ,  G01N33/543 ,  G16B40/00 ,  G16H10/40 ,  G16H20/00 ,  G16H50/00 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  C07K14/70503 ,  C07K14/70521 ,  C07K16/2803 ,  C07K2317/76 ,  G01N2800/52 ,  G01N2800/60 ,  G16H10/60

摘要： Provided herein are methods for classifying how a subject having a cancer will respond to immunotherapeutic (IT) therapy based on the subject's immunosignature or frameshift signature. Also provided herein are methods for classifying a subject having a cancer as having a good prognosis or a poor prognosis based on the subject's immunosignature or frameshift signature.

公开(公告)号：US20240206891A1

公开(公告)日：2024-06-27

申请号：US18542037

申请日：2023-12-15

申请人： Acotec Scientific Co., Ltd.

发明人： Ruijie ZHANG ,  Jian GUO ,  Huiyi GOU ,  Cong ZHANG ,  Tan GENG

IPC分类号： A61B17/22 ,  G16H40/63 ,  G16H50/70

CPC分类号： A61B17/22 ,  G16H40/63 ,  G16H50/70 ,  A61B2017/00022 ,  A61B2017/22079 ,  A61B2217/005

摘要： The present disclosure relates to the field of medical equipment, and provides an intelligent thrombus aspiration system, including a negative pressure suction pump, a blood collection tank, a thrombus aspiration connection unit, a suction catheter and a man-machine interaction module. The man-machine interaction module is configured to transmit a target catheter diameter of the suction catheter to the thrombus aspiration connection unit in response to a command for selecting the target catheter diameter. The thrombus aspiration connection unit is configured to obtain historical treatment data corresponding to the target catheter diameter, cluster the historical treatment data through a neural network model so as to obtain a target suction frequency, a target suction duration and a target suction negative pressure corresponding to the target catheter diameter, and control the negative pressure suction pump and the suction catheter in accordance with the target suction frequency, the target suction duration and the target suction negative pressure. According to the present disclosure, it is able to automatically generate an optimum suction strategy corresponding to the target catheter diameter, determine a suction state of the suction catheter, thereby to reduce the amount of bleeding of a patient without reducing the suction efficiency, and remarkably improve a clinical effect of thrombus aspiration.