公开(公告)号：US20230147471A1

公开(公告)日：2023-05-11

申请号：US18150491

申请日：2023-01-05

Applicant: PAIGE.AI, Inc.

Inventor： Jillian SUE ,  Thomas FUCHS ,  Christopher KANAN

IPC: G16H50/20 ,  G16H30/20 ,  G06T7/00 ,  G06F18/2113 ,  G06F18/214

CPC classification number: G16H50/20 ,  G16H30/20 ,  G06T7/0012 ,  G06F18/2113 ,  G06F18/214 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20104 ,  G06T2207/30024 ,  G06V2201/03

Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.

公开(公告)号：US20230141276A1

公开(公告)日：2023-05-11

申请号：US18150112

申请日：2023-01-04

Applicant: Axial Medical Printing Limited

Inventor： Niall HASLAM ,  Lorenzo TROJAN ,  Daniel CRAWFORD

IPC: G06T7/11 ,  G06T17/20 ,  G06T7/00 ,  G16H30/40 ,  G16H50/70 ,  G16H50/50 ,  G06F18/24 ,  G06V10/26

CPC classification number: G06T7/11 ,  G06T17/20 ,  G06T7/0014 ,  G16H30/40 ,  G16H50/70 ,  G16H50/50 ,  G06F18/24 ,  G06V10/26 ,  G06T2200/08 ,  G06T2207/30004 ,  G06V2201/03

Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

公开(公告)号：US20240362917A1

公开(公告)日：2024-10-31

申请号：US18769701

申请日：2024-07-11

Applicant: TERUMO KABUSHIKI KAISHA

Inventor： Ayato Suzuki ,  Tomohiro Oka ,  Yosuke Itamochi

IPC: G06V20/52 ,  A61B34/20 ,  A61B90/00 ,  G06T11/20 ,  G06V10/40 ,  G06V10/70 ,  G06V40/10 ,  G06V40/20

CPC classification number: G06V20/52 ,  A61B34/20 ,  G06T11/206 ,  G06V10/40 ,  G06V10/70 ,  G06V40/20 ,  A61B2034/2065 ,  A61B2090/372 ,  G06T2207/30196 ,  G06T2210/41 ,  G06V40/10 ,  G06V2201/02 ,  G06V2201/03

Abstract: A method, information processing device, and an information processing system are provided for automatically and accurately recording information about medical devices in an operating room. A surgical space image is captured by an imaging device, wherein the image includes monitor screens of a plurality of display devices dispersedly located in the surgical space. Measurement information is extracted from the monitor screen of each of the display devices on the basis of the acquired surgical space image. The extracted information regarding the monitor screen of each of the display devices is stored in time series to collectively manage the medical devices.

公开(公告)号：US20240362748A1

公开(公告)日：2024-10-31

申请号：US18613668

申请日：2024-03-22

Applicant: KONICA MINOLTA, INC.

Inventor： Ryohei ITO

IPC: G06T3/60 ,  G06V20/50

CPC classification number: G06T3/60 ,  G06V20/50 ,  G06T2210/41 ,  G06V2201/03

Abstract: A medical image output apparatus includes a hardware processor and an outputter. The hardware processor automatically recognizes a structure of a subject in a medical image and automatically adjusts the medical image based on the recognized structure. The outputter outputs the adjusted medical image.

公开(公告)号：US20240358436A1

公开(公告)日：2024-10-31

申请号：US18645629

申请日：2024-04-25

Applicant: MEDIVIEW XR, INC.

Inventor： Jeffrey H. Yanof ,  Aydan Thomas Hanlon ,  Ross Daniel Hinrichsen ,  Peter Nicholas Braido ,  Gabrielle Stefy ,  Michael Russel Evans ,  Charles Martin, III

IPC: A61B34/10 ,  A61B34/00 ,  A61B34/20 ,  A61B90/00 ,  A61B90/50 ,  G03H1/00 ,  G06V10/70 ,  G06V20/20

CPC classification number: A61B34/10 ,  A61B34/20 ,  A61B34/25 ,  A61B90/36 ,  A61B90/50 ,  G03H1/0005 ,  G06V10/70 ,  G06V20/20 ,  A61B2034/105 ,  A61B2034/2065 ,  A61B2090/365 ,  A61B2090/502 ,  G06V2201/03

Abstract: The present disclosure includes methods for improving surgical precision and outcomes in medical procedures through the use of an augmented reality system. The method involves generating a preoperative plan by visualizing a holographic representation of a patient's anatomy within an augmented reality environment, recording procedure data based on the preoperative plan, updating the augmented reality system using the recorded data, and applying the updated system to establish a continuous improvement feedback loop. This approach enhances surgical accuracy and patient outcomes by integrating real-time data feedback into the surgical process, thereby optimizing surgical procedures and promoting continuous improvement in medical practices.

公开(公告)号：US12131525B2

公开(公告)日：2024-10-29

申请号：US17620142

申请日：2020-06-25

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Alexandra Groth ,  Axel Saalbach ,  Ivo Matteo Baltruschat ,  Jens Von Berg ,  Michael Grass

IPC: G06V10/00 ,  G06T7/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/96 ,  G06V20/70

CPC classification number: G06V10/82 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/774 ,  G06V10/96 ,  G06V20/70 ,  G06T2207/10081 ,  G06T2207/10124 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (IL) by labeling the first image data (I) and determining second synthesized labeled image data (ISL) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (IL) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.

公开(公告)号：US12124960B2

公开(公告)日：2024-10-22

申请号：US17148514

申请日：2021-01-13

Applicant: FUJIFILM Corporation

Inventor： Masaaki Oosake ,  Makoto Ozeki

IPC: G06N3/084 ,  G06T7/00 ,  G06V10/143 ,  G06V10/32 ,  G06V10/44 ,  G06V10/60 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06T7/0012 ,  G06V10/143 ,  G06V10/32 ,  G06V10/454 ,  G06V10/60 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06T2207/20084 ,  G06V2201/03

Abstract: An object of the present invention is to provide a learning apparatus and a learning method capable of appropriately learning pieces of data that belong to the same category and are acquired under different conditions. In a learning apparatus according a first aspect of the present invention, first data and second data are respectively input to a first input layer and a second input layer that are independent of each other, and feature quantities are calculated. Thus, the feature quantity calculation in one of the first and second input layers is not affected by the feature quantity calculation in the other input layer. In addition to feature extraction performed in the input layers, each of a first intermediate feature quantity calculation process and a second intermediate feature quantity calculation process is performed at least once in an intermediate layer that is shared by the first and second input layers. Thus, the feature quantities calculated from the first data and the second data in the respective input layers can be reflected in the intermediate feature quantity calculation in the intermediate layer. Consequently, pieces of data that belong to the same category and are acquired under different conditions can be appropriately learned.

公开(公告)号：US20240346805A1

公开(公告)日：2024-10-17

申请号：US18443690

申请日：2024-02-16

Applicant: Hoffmann-La Roche Inc.

Inventor： Szymon Grzegorz ADAMSKI ,  Filippo ARCADU ,  Krzysztof KOTOWSKI ,  Agata KRASON ,  Bartosz Jakub MACHURA ,  Jakub Robert NALEPA ,  Jean TESSIER

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/26 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06T7/0012 ,  G06V10/26 ,  G06V10/82 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Methods disclosed herein relate generally to methods for training an algorithm and for using the trained algorithm for detection, segmentation and characterization of object instances in digital images, applicable for detection, segmentation and characterization of tumor burdens in images from brain MRI scans of Glioblastoma patients.

公开(公告)号：US20240346623A1

公开(公告)日：2024-10-17

申请号：US18624394

申请日：2024-04-02

Applicant: Beijing Chao-Yang Hospital, Capital Medical University

Inventor： Qi YANG ,  Yueyan BIAN ,  Xiuqin JIA

IPC: G06T5/20 ,  G06T7/33 ,  G06V10/26

CPC classification number: G06T5/20 ,  G06T7/337 ,  G06V10/26 ,  G06T2207/10016 ,  G06T2207/10088 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A method and a device for correcting magnetic resonance images are provided. The method includes: acquiring an ADC image to be corrected, which is generated by scanning a target object using an echo planar imaging sequence from a pulse sequence of a portable mobile magnetic resonance apparatus and converting; inputting the ADC image to be corrected into a pre-constructed image correction model to correct a grayscale value of tissue position of the ADC image to be corrected, where the pre-constructed image correction model is generated by function fitting based on magnetic resonance image sequences of different objects; and outputting a target image corresponding to the ADC image to be corrected. A storage medium and a terminal are also provided.

公开(公告)号：US12118719B2

公开(公告)日：2024-10-15

申请号：US17639511

申请日：2020-09-03

Applicant: The Regents of the University of California

Inventor： Hyun Kim ,  Yu Shi ,  Jonathan Gerald Goldin ,  Weng Kee Wong

IPC: G06T7/00 ,  A61B5/00 ,  A61B6/00 ,  A61B6/03 ,  G06V10/25 ,  G06V10/77

CPC classification number: G06T7/0012 ,  A61B5/0037 ,  A61B5/7267 ,  A61B5/7275 ,  A61B6/032 ,  A61B6/5217 ,  G06V10/25 ,  G06V10/7715 ,  G06T2207/10081 ,  G06T2207/30101 ,  G06V2201/03 ,  G06V2201/07

Abstract: A method for training a machine learning algorithm that classifies predictive regions-of interest (“ROI”) of progression of idiopathic pulmonary fibrosis. The method includes acquiring a set of computed tomography (CT) images of a plurality of patients and selecting a plurality of ROIs within the set of images. Each of the ROIs designates a label that indicates progression of pulmonary fibrosis and training a machine learning algorithm by inputting the plurality of ROIs and the associated labels into the algorithm. The algorithm identifies the ROIs in the set of images as indicating regions of pulmonary fibrosis within the set of images based on the features.