公开(公告)号：US12050999B2

公开(公告)日：2024-07-30

申请号：US17221320

申请日：2021-04-02

Applicant: HOWMEDICA OSTEONICS CORP.

Inventor： Sergii Poltaretskyi ,  Jean Chaoui ,  Damien Cariou ,  Yannick Morvan ,  Vincent Abel Maurice Simoes ,  Vincent Gaborit ,  Benjamin Dassonville

IPC: G06N3/084 ,  A61B5/00 ,  A61B5/11 ,  A61B17/14 ,  A61B17/16 ,  A61B17/17 ,  A61B34/00 ,  A61B34/10 ,  A61B90/00 ,  A61B90/92 ,  A61F2/40 ,  G02B27/00 ,  G02B27/01 ,  G06F3/01 ,  G06F3/04815 ,  G06F3/0482 ,  G06F18/21 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/00 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/00 ,  G09B23/28 ,  G16H20/40 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B17/00 ,  A61B17/15 ,  A61B34/20 ,  A61B90/50 ,  A61F2/46 ,  G06F3/0483 ,  G06N3/04 ,  G16H50/20

CPC classification number: G16H20/40 ,  A61B5/1114 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/681 ,  A61B17/142 ,  A61B17/1604 ,  A61B17/1626 ,  A61B17/1659 ,  A61B17/1684 ,  A61B17/1703 ,  A61B34/10 ,  A61B34/25 ,  A61B34/76 ,  A61B90/08 ,  A61B90/36 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61B90/92 ,  A61F2/40 ,  A61F2/4081 ,  G02B27/0075 ,  G02B27/017 ,  G02B27/0172 ,  G06F3/011 ,  G06F3/04815 ,  G06F3/0482 ,  G06F18/2163 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/006 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/003 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B5/744 ,  A61B2017/00115 ,  A61B2017/00119 ,  A61B2017/00123 ,  A61B17/151 ,  A61B17/1775 ,  A61B17/1778 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B2034/2048 ,  A61B2034/2051 ,  A61B2034/2055 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2090/062 ,  A61B2090/067 ,  A61B2090/0801 ,  A61B2090/0807 ,  A61B2090/365 ,  A61B2090/366 ,  A61B2090/367 ,  A61B2090/368 ,  A61B2090/373 ,  A61B2090/374 ,  A61B2090/3762 ,  A61B2090/378 ,  A61B2090/3937 ,  A61B2090/3945 ,  A61B2090/397 ,  A61B2090/502 ,  A61B2505/05 ,  A61B2562/0219 ,  A61F2002/4011 ,  A61F2/4606 ,  A61F2/4612 ,  A61F2002/4633 ,  A61F2002/4658 ,  A61F2002/4668 ,  G02B2027/0141 ,  G02B2027/0174 ,  G06F3/0483 ,  G06N3/04 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30052 ,  G06T2207/30204 ,  G06T2210/41 ,  G06T2219/2004 ,  G06V2201/03 ,  G16H50/20

Abstract: An example method includes displaying, via a visualization device and overlaid on a portion of an anatomy of a patient viewable via the visualization device, a virtual model of the portion of the anatomy obtained from a virtual surgical plan for an orthopedic joint repair surgical procedure to attach a prosthetic to the anatomy; and displaying, via the visualization device and overlaid on the portion of the anatomy, a virtual guide that guides at least one of preparation of the anatomy for attachment of the prosthetic or attachment of the prosthetic to the anatomy.

公开(公告)号：US20240249510A1

公开(公告)日：2024-07-25

申请号：US18623353

申请日：2024-04-01

Applicant: THYROSCOPE INC.

Inventor： Kyubo SHIN

IPC: G06V10/776 ,  A61B3/14 ,  A61B5/00 ,  G06T7/00 ,  G06T7/73 ,  G06V40/18 ,  G16H30/40 ,  H04N7/18

CPC classification number: G06V10/776 ,  A61B3/14 ,  A61B5/4227 ,  G06T7/0014 ,  G06T7/73 ,  G06V40/193 ,  G16H30/40 ,  H04N7/183 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30041 ,  G06V2201/03

Abstract: Provided is a diagnostic system including: a user terminal configured to take an image; and a server configured to obtain diagnosis assistance information on the basis of the image. The user terminal is configured to obtain a first photographing parameter, determine whether a pre-stored condition is satisfied, and transmit the first photographing parameter and a captured image to the server when it is determined that the pre-stored condition is satisfied. The server is configured to obtain a first verification parameter, determine whether to use the captured image as a diagnostic image which includes comparing the first verification parameter with the first photographing parameter, and obtain the diagnosis assistance information by using the diagnostic image when it is determined to use the captured image as the diagnostic image.

公开(公告)号：US12046368B2

公开(公告)日：2024-07-23

申请号：US17383205

申请日：2021-07-22

Applicant: Iterative Scopes, Inc.

Inventor： Jonathan Ng ,  Jean-Pierre Schott ,  Daniel Wang

IPC: G16H50/20 ,  A61B1/31 ,  A61B5/00 ,  G06F18/21 ,  G06F18/24 ,  G06N3/08 ,  G06T7/00 ,  G16B20/00 ,  G16B30/00 ,  G16B40/20 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  G16H10/20 ,  G16H10/40

CPC classification number: G16H50/20 ,  A61B1/31 ,  A61B5/7267 ,  A61B5/7275 ,  G06F18/21 ,  G06F18/24 ,  G06N3/08 ,  G06T7/0012 ,  G16B20/00 ,  G16B30/00 ,  G16B40/20 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  G06T2207/10068 ,  G06T2207/20081 ,  G06T2207/30092 ,  G06V2201/03 ,  G16H10/20 ,  G16H10/40

Abstract: This specification describes systems and methods for obtaining various patient related data for inflammatory bowel disease (IBD). The methods and systems are configured for using machine learning to determine measurements of various characteristics and provide analysis related to IBD. The methods and systems may also obtain and incorporate electronic health data as well as other relevant data of patients along with endoscopic data to use for prediction IDB progression and recommending treatment.

公开(公告)号：US12046349B2

公开(公告)日：2024-07-23

申请号：US17117636

申请日：2020-12-10

Applicant: HOWMEDICA OSTEONICS CORP.

Inventor： Yannick Morvan ,  Sergii Poltaretskyi ,  Jean Chaoui ,  Damien Cariou

IPC: G16H20/40 ,  A61B5/00 ,  A61B5/11 ,  A61B17/14 ,  A61B17/16 ,  A61B17/17 ,  A61B34/00 ,  A61B34/10 ,  A61B90/00 ,  A61B90/92 ,  A61F2/40 ,  G02B27/00 ,  G02B27/01 ,  G06F3/01 ,  G06F3/04815 ,  G06F3/0482 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/00 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/00 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B17/00 ,  A61B17/15 ,  A61B34/20 ,  A61B90/50 ,  A61F2/46 ,  G06F3/0483 ,  G06N3/04 ,  G16H50/20

CPC classification number: G16H20/40 ,  A61B5/1114 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/681 ,  A61B17/142 ,  A61B17/1604 ,  A61B17/1626 ,  A61B17/1659 ,  A61B17/1684 ,  A61B17/1703 ,  A61B34/10 ,  A61B34/25 ,  A61B90/08 ,  A61B90/36 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61B90/92 ,  A61F2/40 ,  A61F2/4081 ,  G02B27/0075 ,  G02B27/017 ,  G02B27/0172 ,  G06F3/011 ,  G06F3/04815 ,  G06F3/0482 ,  G06F30/10 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/006 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/003 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B5/744 ,  A61B2017/00115 ,  A61B2017/00119 ,  A61B2017/00123 ,  A61B17/151 ,  A61B17/1775 ,  A61B17/1778 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B2034/2048 ,  A61B2034/2051 ,  A61B2034/2055 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2090/062 ,  A61B2090/067 ,  A61B2090/0801 ,  A61B2090/0807 ,  A61B2090/365 ,  A61B2090/366 ,  A61B2090/367 ,  A61B2090/368 ,  A61B2090/373 ,  A61B2090/374 ,  A61B2090/3762 ,  A61B2090/378 ,  A61B2090/3937 ,  A61B2090/3945 ,  A61B2090/397 ,  A61B2090/502 ,  A61B2505/05 ,  A61B2562/0219 ,  A61F2002/4011 ,  A61F2/4606 ,  A61F2/4612 ,  A61F2002/4633 ,  A61F2002/4658 ,  A61F2002/4668 ,  G02B2027/0141 ,  G02B2027/0174 ,  G06F3/0483 ,  G06N3/04 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30052 ,  G06T2207/30204 ,  G06T2210/41 ,  G06T2219/2004 ,  G06V2201/03 ,  G16H50/20

Abstract: A computing system obtains an information model specifying a first surgical plan for an orthopedic surgery to be performed on a patient. Additionally, the computing system modifies the first surgical plan during an intraoperative phase of the orthopedic surgery to generate a second surgical plan. During the intraoperative phase of the orthopedic surgery, a visualization device may present a visualization for display that is based on the second surgical plan.

公开(公告)号：US20240242433A1

公开(公告)日：2024-07-18

申请号：US18562681

申请日：2022-05-24

Applicant: MEDIT CORP.

Inventor： Dong Hoon LEE

IPC: G06T17/00 ,  A61C9/00 ,  G06V10/82 ,  G06V40/10

CPC classification number: G06T17/00 ,  A61C9/0053 ,  G06V10/82 ,  G06V40/10 ,  G06T2210/41 ,  G06V2201/03

Abstract: Various embodiments disclosed in the present disclosure provide an electronic device comprising: a communication circuit communicatively connected to a three-dimensional scanner; at least one memory configured to store a correlation model constructed by modeling a correlation between a two-dimensional image set regarding oral cavities of subjects and a data set in which a tooth region and a gingival region are identified in each image of the two-dimensional image set according to a machine learning algorithm; and at least one processor, wherein the at least one processor is configured to access a two-dimensional image regarding a target oral cavity or target diagnosis model received from the three-dimensional scanner through the communication circuit, and use the correlation model to identify a tooth region and a gingival region from the two-dimensional image regarding the target oral cavity or target diagnostic model.

公开(公告)号：US12039732B2

公开(公告)日：2024-07-16

申请号：US17230121

申请日：2021-04-14

Applicant: The Procter & Gamble Company

Inventor： Supriya Punyani ,  Vandana Reddy Padala

IPC: G06T7/00 ,  G06F18/214 ,  G06F18/24 ,  G06N20/00 ,  G06Q10/0833 ,  G16H20/30

CPC classification number: G06T7/0016 ,  G06F18/214 ,  G06F18/24 ,  G06N20/00 ,  G06Q10/0833 ,  G16H20/30 ,  G06T2207/20081 ,  G06T2207/30088 ,  G06V2201/03

Abstract: Digital imaging and learning systems and methods are described for analyzing pixel data of a scalp region of a user's scalp to generate one or more user-specific scalp classifications. A digital image of a user is received at an imaging application (app) and comprises pixel data of at least a portion of a scalp region of the user's scalp. A scalp based learning model, trained with pixel data of a plurality of training images depicting scalp regions of scalps of respective individuals, analyzes the image to determine at least one image classification of the user's scalp region. The imaging app generates, based on the at least one image classification, a user-specific scalp classification designed to address at least one feature identifiable within the pixel data comprising the at least the portion of a scalp region of the user's scalp.

公开(公告)号：US12039007B2

公开(公告)日：2024-07-16

申请号：US17067179

申请日：2020-10-09

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Dattesh Dayanand Shanbhag ,  Chitresh Bhushan ,  Andre De Almeida Maximo ,  Radhika Madhavan ,  Desmond Teck Beng Yeo ,  Thomas Kwok-Fah Foo

IPC: G06F18/214 ,  G06F18/211 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40

CPC classification number: G06F18/2148 ,  G06F18/211 ,  G06F18/2155 ,  G06F18/22 ,  G06F18/232 ,  G06N3/08 ,  G16H30/40 ,  G06V2201/03

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.

公开(公告)号：US20240233416A1

公开(公告)日：2024-07-11

申请号：US18289299

申请日：2022-05-04

Applicant: The Institute of Cancer Research: Royal Cancer Hospital

Inventor： Hanyun Zhang ,  Yinyin Yuan

IPC: G06V20/69 ,  G06V10/74 ,  G06V10/772 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V20/698 ,  G06V10/761 ,  G06V10/772 ,  G06V10/774 ,  G06V10/82 ,  G06V2201/03

Abstract: Methods and systems for analysing the cellular composition of a sample are described, comprising: providing an image of the sample in which a plurality of cellular populations are associated with respective signals and classifying a plurality of query cells in the image between a plurality of classes corresponding to respective cellular populations in the plurality of cellular populations. This is performed by providing a query single cell image to an encoder module of a machine learning model to produce a feature vector for the query image, and assigning the query cell to one of the plurality of classes based on the feature vector for the query image and feature vectors produced by the encoder module for each of a plurality of reference single cell images. The machine leaning model comprises: the encoder module, configured to take as input a single cell image and to produce as output a feature vector the single cell image, and a similarity module configured to take as input a pair of feature vectors for a pair of single cell images and to produce as output a score indicative of the similarity between the single cell images. Thus, the machine learning model can be obtained without the need for an extensively annotated dataset. The methods find use in the analysis of multiplex immunohistochemistry/immunofluorescence in a variety of clinical contexts.

公开(公告)号：US20240225447A1

公开(公告)日：2024-07-11

申请号：US18509061

申请日：2023-11-14

Applicant: HOLOGIC, INC.

Inventor： Haili CHUI ,  Zhenxue JING

IPC: A61B5/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06V10/774 ,  G06V10/82

CPC classification number: A61B5/0033 ,  G06F18/214 ,  G06F18/217 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06V10/774 ,  G06V10/82 ,  G06V2201/03

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

公开(公告)号：US20240221191A1

公开(公告)日：2024-07-04

申请号：US18399461

申请日：2023-12-28

Applicant: UNITED IMAGING RESEARCH INSTITUTE OF INNOVATIVE ME

Inventor： BING-SHUAI ZHAO ,  YIN-SHENG LI

IPC: G06T7/33 ,  G06T5/50 ,  G06T7/11 ,  G06T7/174 ,  G06V10/26 ,  G06V10/74

CPC classification number: G06T7/337 ,  G06T5/50 ,  G06T7/11 ,  G06T7/174 ,  G06V10/26 ,  G06V10/761 ,  G06T2207/20021 ,  G06T2207/20224 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06V2201/03

Abstract: This disclosure relates to a method for identifying control points, an apparatus and a computer device. Multiple image blocks are obtained by segmenting a target gradient image, and a number of control points to be selected for each image block is determined based on a first preset number of control points and pixel values within each image block. Target control points are therefore identified in the target gradient image based on image block information corresponding to each image block. The target gradient image is a gradient image of a target image of an inspected object. The target image includes any one of a mask image, a live image, or a subtracted image. The image block information includes the number of control points to be selected for the corresponding image block and a size of the image block.