公开(公告)号：WO2023058067A1

公开(公告)日：2023-04-13

申请号：PCT/IN2022/050904

申请日：2022-10-07

Applicant: QURE.AI TECHNOLOGIES PRIVATE LIMITED

Inventor： WARIER, Prashant ,  MODI, Ankit ,  PUTHA, Preetham ,  VANAPALLI, Prakash ,  CHALLA, Vikash

IPC: G06T7/00 ,  G16H30/40 ,  G06N20/00 ,  A61B6/032 ,  A61B6/50 ,  A61B6/5217 ,  A61B6/5223 ,  A61B6/5258 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30064 ,  G06T5/002 ,  G06T7/0012 ,  G06T7/0016 ,  G06T7/11 ,  G06T7/40 ,  G06T7/62 ,  G06V10/25 ,  G06V10/273 ,  G06V10/75 ,  G06V10/82 ,  G06V2201/03 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30

Abstract: Disclosed is a system (102) and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.

公开(公告)号：WO2023274512A1

公开(公告)日：2023-01-05

申请号：PCT/EP2021/067878

申请日：2021-06-29

Applicant: BRAINLAB AG

Inventor： MOSER, Christoph ,  BRIEU, Nicolas ,  LIU, Qianyu ,  PUCH GINER, Santiago

IPC: G06K9/62 ,  G06V10/82 ,  G06F18/213 ,  G06F18/22 ,  G06V2201/03

Abstract: Disclosed is i.a. a computer-implemented method of determining a similarity between medical images which encompasses determining patches, i.e. subsets, of a medical image which is newly input to a data storage and of medical images which has been previously stored, analysing the patches for similar image features in a dimensionality- reduced reference system used for defining the image features, computing a distance between the image features in the dimensionality-reduced reference system, and based on the result of comparing the distances calculated for the newly input image and the previously stored medical images, determining whether the medical images are similar and for example originate from the same patient. Artificial intelligence is used to generate the dimensionality-reduced representation of the image features, i.e. to encode the medical images for further processing by the methods and the system disclosed herein.

公开(公告)号：WO2022236100A2

公开(公告)日：2022-11-10

申请号：PCT/US2022/028140

申请日：2022-05-06

Applicant: OWKIN, INC.

Inventor： SAILLARD, Charlie ,  SCHMAUCH, Benoit ,  AUBERT, Victor ,  AURÉLIE, Kamoun ,  LACROIX-TRIKI, Magali ,  GARBERIS, Ingrid ,  DRUBAY, Damien ,  ANDRÉ, Fabrice ,  CROS, Jérôme

IPC: G06V10/50 ,  G06V10/82 ,  G06V2201/03

Abstract: Deep learning models for predicting one or more features of pancreatic ductal adenocarcinoma from histopathology slide images is provided.

公开(公告)号：WO2023018555A1

公开(公告)日：2023-02-16

申请号：PCT/US2022/038739

申请日：2022-07-28

Applicant: NVIDIA CORPORATION

Inventor： MYRONENKO, Andriy ,  XU, Ziyue ,  YANG, Dong ,  ROTH, Holger ,  XU, Daguang

IPC: G06V10/82 ,  G06V20/69 ,  G06V2201/03

Abstract: Apparatuses, systems, and techniques are presented to classify objects in images. In at least one embodiment, one or more neural networks are used to identify one or more objects in one or more full images based, at least in part, on the one or more neural networks having been trained using the one or more full images and one or more portions of the one or more full images.

公开(公告)号：WO2023278965A1

公开(公告)日：2023-01-05

申请号：PCT/US2022/073159

申请日：2022-06-24

Applicant: INTUITIVE SURGICAL OPERATIONS, INC.

Inventor： QIN, Yidan ,  ALLAN, Maximilian H. ,  AZIZIAN, Mahdi

IPC: G06V20/00 ,  G06V10/70 ,  A61B8/08 ,  G06F21/62 ,  A61B8/5223 ,  A61B8/5292 ,  G06F21/6263 ,  G06V20/35 ,  G06V2201/03

Abstract: An illustrative image processing system is configured to apply a video stream to a machine learning model, the video stream generated by an imaging device during a medical procedure performed with respect to a patient; classify, based on an output of the machine learning model, an image frame included in the video stream as an ex- body frame that depicts content external to a body of the patient: and apply, based on the classifying the image frame as the ex-body frame, a privacy enhancing operation to the image frame.

公开(公告)号：WO2023274622A1

公开(公告)日：2023-01-05

申请号：PCT/EP2022/063351

申请日：2022-05-17

Applicant: DENTSPLY SIRONA INC. ,  SIRONA DENTAL SYSTEMS GMBH

Inventor： SCHNEIDER, Hans-Christian ,  SCHADER, David ,  SEIBERT, Helmut

IPC: G06K9/62 ,  G06V20/64 ,  G06F18/24133 ,  G06V2201/03 ,  G06V2201/12

Abstract: The present teachings relate to a method for transforming a digital 3D dental model to a 2D dental image, the method comprising: providing, at least two selected images from the 3D dental model; converting, each of the selected images, to a respective color component of a color model; obtaining, by combining the respective color components, the 2D image in a computer-readable format of the color model. The present teachings also relate to a method of classification of the 2D image, a dental procedure assisting system, uses and computer software products.

公开(公告)号：WO2022212771A2

公开(公告)日：2022-10-06

申请号：PCT/US2022/022928

申请日：2022-03-31

Applicant: SIRONA MEDICAL, INC.

Inventor： PAIK, David Seungwon ,  ANDREWS, Cameron ,  LONGO, Mark D. ,  LIEMAN-SIFRY, Jesse ,  PETERS, Jaclynn ,  KUCHERLAPATI, Sumedha ,  KLOCKENBRINK, Jennifer ,  LERMAN, Jeffrey C. ,  MEHTA, Samir ,  MUKHERJEE, Atreyee ,  LONG, Aaron ,  YI, Darvin

IPC: G16H15/00 ,  G06N3/02 ,  G16H30/40 ,  G06N20/00 ,  G06T2207/10056 ,  G06T2207/10068 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/30004 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/25 ,  G06V10/82 ,  G06V2201/03

Abstract: Described herein are systems, software, and methods for interpreting medical images and generating reports. In some aspects, the systems, software, and methods described herein represent a single interface to the various subsystems of the radiology technology stack (e.g., RIS, PACS, Al, reporting, etc.). The system can provide a unified experience to a user such as a radiologist and provide support and analytics that would otherwise not be possible under a fragmented tech stack.

公开(公告)号：WO2022072181A1

公开(公告)日：2022-04-07

申请号：PCT/US2021/051337

申请日：2021-09-21

Applicant: GE PRECISION HEALTHCARE LLC [US]/[US]

Inventor： TAN, Tao ,  AVINASH, Gopal B. ,  FEJES, Máté ,  SONI, Ravi ,  SZABO, Dániel Attila ,  MULLICK, Rakesh ,  MELAPUDI, Vikram ,  SHRIRAM, Krishna Seetharam ,  RANJAN, Sohan Rashmi ,  DAS, Bipul ,  AGRAWAL, Utkarsh ,  RUSKO, László ,  HERCZEG, Zita ,  DARAZS, Barbara

IPC: G06T15/10 ,  A61B5/055 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/5223 ,  G06K9/6215 ,  G06K9/6255 ,  G06K9/6256 ,  G06K9/6263 ,  G06N5/04 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06T2210/41 ,  G06T5/50 ,  G06T7/30 ,  G06V2201/03 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：WO2021263000A2

公开(公告)日：2021-12-30

申请号：PCT/US2021/038925

申请日：2021-06-24

Applicant: F. HOFFMANN-LA ROCHE AG ,  ROCHE DIAGNOSTICS GMBH ,  ROCHE MOLECULAR SYSTEMS, INC.

Inventor： TORBEN-NIELSEN, Benjamin ,  GARCIA-ALCALDE, Fernando ,  MAUDUIT, Vincent ,  GUERENDEL, Corentin Christophe Karim ,  ARNOLD, Phil Pascal

IPC: G16H30/40 ,  G06K9/00 ,  G06N3/04 ,  G06T7/00 ,  G16H50/20 ,  G16H10/60 ,  G06K9/6271 ,  G06K9/6293 ,  G06N20/20 ,  G06N3/0454 ,  G06N3/08 ,  G06N5/003 ,  G06T2207/10056 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30242 ,  G06T7/0012 ,  G06V10/50 ,  G06V2201/03

Abstract: A method of verifying multi-modal medical data is proposed. The method comprises: accessing multi-modal medical data of a subject, the multi-modal medical data comprising a medical image of a specimen slide, wherein a specimen in the specimen slide was collected from the subject; generating a prediction pertaining to a biological attribute of the medical image based on the medical image; determining a degree of consistency between the biological attribute of the medical image and other modalities of the multi-modal medical data; and outputting, based on the degree of consistency, an indication of whether the multi-modal medical data contain inconsistency.

公开(公告)号：WO2023059746A1

公开(公告)日：2023-04-13

申请号：PCT/US2022/045817

申请日：2022-10-05

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： BANERJEE, Tathagata ,  KOLLADA, Matthew Edward ,  TORFI, Amirsina ,  CROCKER, Peter

IPC: G06N3/042 ,  G06N3/0455 ,  G06N3/084 ,  G06N3/088 ,  G06N3/0895 ,  G06N3/0985 ,  G16B40/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/70 ,  G06N3/0464 ,  G06N20/00 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30104 ,  G06T7/0016 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06V2201/03 ,  G16H40/20 ,  G16H50/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a clinical recommendation for medical treatment of a patient. In one aspect a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using a machine learning model, in accordance with values of a set of machine learning model parameters, to generate a patient classification that classifies the patient as being included in a patient category from a set of patient categories; determining an uncertainty measure that characterizes an uncertainty of the patient classification generated by the machine learning model; and generating a clinical recommendation for medical treatment of the patient based on: (i) the patient classification, and (ii) the uncertainty measure that characterizes the uncertainty of the patient classification.