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IC:A61B6/00 AND EN_ALLTXT:(coronavirus OR coronaviruses OR coronaviridae OR coronavirinae OR orthocoronavirus OR orthocoronaviruses OR orthocoronaviridae OR orthocoronavirinae OR betacoronavirus OR betacoronaviruses OR betacoronaviridae OR betacoronavirinae OR sarbecovirus OR sarbecoviruses OR sarbecoviridae OR sarbecovirinae OR "severe acute respiratory syndrome" OR sars OR "2019 ncov" OR covid)

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Analysis

1.WO/2021/195586ULTRAVIOLET RADIATION TREATMENTS
WO 30.09.2021
Int.Class A61N 5/06
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
NELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
5Radiation therapy
06using light
Appl.No PCT/US2021/024520 Applicant RAMIREZ-FORT, Marigdalia, Kaleth Inventor RAMIREZ-FORT, Marigdalia, Kaleth
Systems, methods, and computer-readable media for enabling ultraviolet radiation treatments are provided.
2.2599488Machine-learning techniques for oxygen therapy prediction using medical imaging data and clinical metadata
GB 06.04.2022
Int.Class A61B 6/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
6Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
Appl.No 202112104 Applicant NVIDIA CORP Inventor WENTAO ZHU
A processor of a system trains one or more neural networks to predict a treatment for a patient based, at least in part, on medical imaging data and clinical metadata, and determines a treatment for the patient using the trained neural net. Whether the patient is part of a patient population to receive a treatment is determined. The medical imaging data may comprise slices of a computer tomography scan. The clinical metadata may comprise laboratory measurements of lactate dehydrogenase levels and C-reactive protein levels. The treatment may be for COVID-19. An aggregate image-based treatment probability for a plurality of images may be determined. The aggregate probability and clinical metadata may be normalised to obtain input features used to train a portion of the neural networks to obtain weights indicating how impactful each feature is to determining the treatment. A multi-modal deep learning framework and the EfficientNet convolutional neural network may be used.
3.20210327055Systems and methods for detection of infectious respiratory diseases
US 21.10.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
Appl.No 16889412 Applicant Qure.ai Technologies Private Limited Inventor Preetham Putha

This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.

4.20210186344CLINICAL ARTIFICIAL INTELLIGENCE (AI) SOFTWARE AND TERMINAL GATEWAY HARDWARE METHOD FOR MONITORING A SUBJECT TO DETECT A POSSIBLE RESPIRATORY DISEASE
US 24.06.2021
Int.Class A61B 5/0205
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
02Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
0205Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
Appl.No 17193333 Applicant Pai-chang yeh Inventor Pai-chang yeh

The present invention relates generally to an apparatus and method for detecting diseases and, more, specifically, detecting respiratory diseases such as airborne transmitting diseases at the early stage. The present invention provides a solution in the form of a clinical AI Software and terminal gateway hardware. Terminal gateway with clinical AI software can detect diseases by collecting and analyzing data from multiple sensors and modules. The terminal gateway can offload healthcare professionals from over-work and misjudge due to long working hours. It also provides a better second-opinion for less-experienced professionals. The gateway terminal is used to detect respiratory diseases such as airborne transmitting diseases at the early stage to help offload the healthcare professional in the hospital and to provide an alert when the professionals are not available outside of the hospital. The present invention includes a structure of a gateway or station, multiple sensor modules such as low wattage x-ray, an infrared thermal detector, and the clinical AI software.

5.20220022818ASSESSMENT OF ABNORMALITY PATTERNS ASSOCIATED WITH COVID-19 FROM X-RAY IMAGES
US 27.01.2022
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
Appl.No 16947149 Applicant Siemens Healthcare GmbH Inventor Florin-Cristian Ghesu

Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

6.20220059221MACHINE-LEARNING TECHNIQUES FOR OXYGEN THERAPY PREDICTION USING MEDICAL IMAGING DATA AND CLINICAL METADATA
US 24.02.2022
Int.Class G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
Appl.No 17000982 Applicant NVIDIA Corporation Inventor Wentao Zhu

Apparatuses, systems, and techniques to train one or more neural networks based, at least in part on, medical imaging data and clinical metadata or inference using one or more neural networks trained as such. In at least one embodiment, one or more circuits to train one or more neural network to predict a treatment for a patient suspected to have or confirmed to have COVID-19 based, at least in part on, medical imaging data and clinical metadata.

7.20210330269RISK PREDICTION FOR COVID-19 PATIENT MANAGEMENT
US 28.10.2021
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
Appl.No 16891309 Applicant Siemens Healthcare GmbH Inventor Puneet Sharma

Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

8.20210304408Assessment of abnormality regions associated with a disease from chest CT images
US 30.09.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
Appl.No 16837979 Applicant Siemens Healthcare GmbH Inventor Shikha Chaganti

Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

9.20220020145REAL-TIME ESTIMATION OF LOCAL CARDIAC TISSUE PROPERTIES AND UNCERTAINTIES BASED ON IMAGING AND ELECTRO-ANATOMICAL MAPS
US 20.01.2022
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
Appl.No 17305631 Applicant Siemens Healthcare GmbH Inventor Felix Meister

Systems and methods for automatically detecting a disease in medical images are provided. Input medical images are received. A plurality of metrics for a disease is computed for each of the input medical images. The input medical images are clustered into a plurality of clusters based on one or more of the plurality of metrics to classify the input medical images. The plurality of clusters comprise a cluster of one or more of the input medical images associated with the disease and one or more clusters of one or more of the input medical images not associated with the disease. In one embodiment, the disease is COVID-19 (coronavirus disease 2019).

10.WO/2021/236239SYSTEM FOR MINIMIZING RISK OF TRANSMISSION OF INFECTION
WO 25.11.2021
Int.Class A61C 19/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
CDENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
19Dental auxiliary appliances
Appl.No PCT/US2021/025724 Applicant JAMES L. ORRINGTON, II D.D.S., P.C. Inventor ORRINGTON, James
Disclosed herein are systems for minimizing the risk of transmission of SARS-CoV-2 and/or other infectious diseases between individuals in close proximity to one another. Said systems may comprise a substantially transparent shield component, a chamber component, and a suctioning component. The systems of the present disclosure are intended to remove any SARS-CoV-2 virus and/or other infectious agents that may be travelling within aerosols.