公开(公告)号：US20220198668A1

公开(公告)日：2022-06-23

申请号：US17556529

申请日：2021-12-20

Applicant: VUNO Inc.

Inventor： Hyunho PARK ,  Gwangbeen PARK ,  Seungho LEE

IPC: G06T7/00 ,  G06T7/70 ,  G06T7/11 ,  G06V10/25 ,  G06T7/62 ,  G06T7/30 ,  G06V10/764 ,  G16H30/40 ,  G16H50/20

Abstract: Disclosed is a method for analyzing a lesion based on a medical image, which is performed by a computing device. The method may include: obtaining positional information of a suspicious nodule which exists in the medical image; generating a mask for the suspicious nodule based on a patch of the medical image corresponding to the positional information; and determining a class for a state of the suspicious nodule based on the patch of the medical image and the mask for the suspicious nodule.

公开(公告)号：US11361441B2

公开(公告)日：2022-06-14

申请号：US16618488

申请日：2018-01-04

Applicant: Vuno, Inc.

Inventor： Hyun-Jun Kim ,  Yeha Lee

IPC: G06K9/00 ,  G06T7/00 ,  G16H50/50 ,  G16H50/20 ,  G16H30/40 ,  G06N3/08 ,  G06N3/04

Abstract: Provided are a method for determining whether an examinee is infected by microorganism, and a determination apparatus using the same. Specifically, the determination apparatus according to the present invention obtains an microphotographed image of a biological sample of the examinee; receives the obtained microphotographed image and generates analysis information on the microorganism based on a deep learning model of the examinee; visualize the generated analysis information to provide it, so as to perform at least one of (i) a process of supporting a remote reading on whether the microorganism corresponding to the analysis information exists or not, and (ii) a process of supporting a user of the computing apparatus to read whether the microorganism corresponding to the analysis information exists or not; and provides a final result as its result.

公开(公告)号：US20220180512A1

公开(公告)日：2022-06-09

申请号：US17539827

申请日：2021-12-01

Applicant: VUNO Inc.

Inventor： Hyunwoo OH ,  Sejin PARK ,  Jinkyeong SUNG ,  Weon Jin KIM ,  Eunpyeong HONG ,  Dong Soo LEE

IPC: G06T7/00 ,  G06T7/30 ,  G06T7/11 ,  A61B5/00 ,  G06N3/04 ,  G16H30/40 ,  G16H50/20

Abstract: Disclosed is a method for predicting disease based on a medical image performed by a computing device. The method includes: generating a feature vector related to predictive values of brain disease for each of 2D medical images included in a 3D medical image, using a pre-trained first model; estimating importance indicating prediction accuracy for each of the 2D medical images based on the feature vector, using a pre-trained second model; and selecting at least one model input image suitable for prediction of the brain disease from among the 2D medical images based on the importance.

公开(公告)号：US20220130065A1

公开(公告)日：2022-04-28

申请号：US17509590

申请日：2021-10-25

Applicant: VUNO Inc.

Inventor： Eunpyeong HONG ,  Wonmo JUNG ,  Sejin PARK ,  Hyunwoo OH ,  Dong Soo LEE ,  Weon Jin KIM ,  Jinkyeong SUNG

IPC: G06T7/62 ,  G06T7/00 ,  G06T17/20

Abstract: Disclosed is a method for analyzing a thickness of a cortical region, performed by a computing device. The method may include: extracting a plurality of interfaces included in a cortical region based on a mask generated from a medical image including at least one brain region; and estimating a thickness of the cortical region based on the plurality of interfaces.

公开(公告)号：US20220012634A1

公开(公告)日：2022-01-13

申请号：US17295880

申请日：2019-12-04

Applicant: VUNO INC.

Inventor： Yeongnam LEE

IPC: G06N20/00 ,  G16H50/20 ,  G16H50/70 ,  G06K9/62

Abstract: The present disclosure provides a method for assessing the degree of risk of a subject and classifying the subject according to the degree of risk, and a computing device using same. Specifically, by a method according to the present invention, a computer device: obtains integrated data of the subject, wherein the integrated data, which is patient data relating to the subject or data obtained by processing the patient data, is numerical data; then applies the integrated data to a machine learning model for assessing the degree of risk of the subject to produce a result obtained by the classification, as a result obtained by assessing the degree of risk; and provides the produced classification result to an external entity.

公开(公告)号：US20220005604A1

公开(公告)日：2022-01-06

申请号：US17361103

申请日：2021-06-28

Applicant: VUNO Inc.

Inventor： Jaemin SON

IPC: G16H50/20 ,  G16H50/30 ,  A61B5/00

Abstract: Disclosed is a method of providing diagnostic related information for medical data, performed by one or more processors of a computing device. The method may include: calculating first diagnosis information on input medical data by using a first diagnosis network trained to output diagnosis information based on the medical data; calculating second diagnosis information on the input medical data by using a second diagnosis network trained to output diagnosis information based on a feature vector for the medical data calculated in the first diagnosis network, wherein the feature vector is calculated based on a feature map derived from a calculation process of the first diagnosis network; and generating correlation information comprising the first diagnosis information and the second diagnosis information calculated on the input medical data, based on a part of a calculation process of the first diagnosis network or a calculation process of the second diagnosis network.

公开(公告)号：US12236605B2

公开(公告)日：2025-02-25

申请号：US17533858

申请日：2021-11-23

Applicant: VUNO Inc. ,  SEOUL NATIONAL UNIVERSITY HOSPITAL

Inventor： Dong Soo Lee ,  Hyunwoo Oh ,  Sejin Park ,  Jinkyeong Sung ,  Eunpyeong Hong ,  Weon Jin Kim ,  Ki Woong Kim ,  Jong Bin Bae ,  Subin Lee ,  Jun Sung Kim

IPC: G06T7/11

Abstract: According to an embodiment of the present disclosure, a method of detecting a white matter lesion based on a medical image performed by a computing device is disclosed. The method may include: receiving a medical image including at least one brain region; and estimating a first white matter lesion and a second white matter lesion based on the medical image using a pre-trained neural network model.

公开(公告)号：US12014493B2

公开(公告)日：2024-06-18

申请号：US17360897

申请日：2021-06-28

Applicant: VUNO Inc.

Inventor： Byeonguk Bae ,  Kyuhwan Jung

IPC: G06T7/00 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06T7/0012 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008

Abstract: According to an embodiment of the present disclosure, a method of assessing bone age by using a neural network performed by a computing device is disclosed. The method includes receiving an analysis image which is a target of bone age assessment; and assessing bone age of the target by inputting the analysis image into a bone age analysis model comprising one or more neural networks. The bone age analysis model, which is trained by supervised learning based on an attention guide label, includes at least one attention module for intensively analyzing a main region of the analysis image.

公开(公告)号：US20240180471A1

公开(公告)日：2024-06-06

申请号：US18556517

申请日：2021-11-09

Applicant: VUNO Inc.

Inventor： Woong BAE ,  Yunwon TAE

IPC: A61B5/327 ,  A61B5/00 ,  A61B5/346

CPC classification number: A61B5/327 ,  A61B5/346 ,  A61B5/7246 ,  A61B5/7278

Abstract: Disclosed is a bio-signal analysis method performed by computing device according to an embodiment of the present disclosure. The method may include: acquiring at least one lead-wise bio-signal from a plurality of leads: and deriving an analysis value by inputting the at least one acquired lead-wise bio-signal into a neural network model.

公开(公告)号：US20240161930A1

公开(公告)日：2024-05-16

申请号：US18231110

申请日：2023-08-07

Applicant: VUNO Inc.

Inventor： Yunseob SHIN ,  Yunwon TAE ,  Kyungjae CHO ,  Jaewoo CHOI

IPC: G16H50/30 ,  G16H50/50 ,  G16H50/70

CPC classification number: G16H50/30 ,  G16H50/50 ,  G16H50/70

Abstract: Disclosed is a method for generating a prediction result by using static data and dynamic data according to an exemplary embodiment of the present disclosure. Specifically, according to the present disclosure, a computing device generates an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model. The computing device generates a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model. The computing device generates a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector.