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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 / 29
1.20210304408Assessment 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.

2.20220020145REAL-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).

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.

4.20210330269Risk prediction for COVID-19 patient management
US - 28.10.2021
Int.Class G16H 30/40
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
30ICT specially adapted for the handling or processing of medical images
40for processing medical images, e.g. editing
 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.

5.2021100784Covid19 Intelligent Detection System
AU - 04.03.2021
Int.Class A61B 6/03
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
6Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
02Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
03Computerised tomographs
 Appl.No 2021100784 Applicant BHATIA, GAGANDEEP SINGH MR Inventor BHATIA, GAGANDEEP SINGH
Covid19 Intelligent Detection System Originally called SARS-CoV-2 abbreviated as COVID-19, Corona virus disease 2019 (COVID-19) is a respiratory illness that is spreading from person to person. The virus that causes COVID-19 is a novel corona virus that was first identified during an investigation into an outbreak in Wuhan, China. Common signs of infection include respiratory symptoms, fever, and cough, shortness of breath and breathing difficulties, pneumonia, severe acute respiratory syndrome, kidney failure and even death. In order to fight against this epidemic, we designed an Image Classification with Localization model that will help the doctors all over the world to detect the early symptoms in the affected humans. COVID-19 model predicted ground-glass opacities, consolidations, and crazy paving (fluid) patterns in the Chest CT (Computed Tomography) of the affected patients, which is the first aid even before the Reverse transcription polymerase chain reaction (RT-PCR) test. This automatic prediction based method will help the medical department to work accordingly by reducing cost and time simultaneously. This Al based Covid-19 algorithm achieved an accuracy of 97.4%. CheckingofCTM-ssnimage? Reality and Type pImag CeNN Model YES magesSegmentation Model For Supervised By the modelI ExtractingoniytheT-scanareaC sif t O Fxtrrgatckteednfm frm Android Anddeieting thereining po Nt of the mae Message will appear as UNRECOGNISED IMAGE Database SegmentationdIntontpanatysi Process Flowchart Ground GlassOpacity9B.4% conslidati-Opacity 1% C CVVDI1-99A 34 ] Crazy Pav ing Opacity0.6% nptiags rprocesin emettin clsifica [tion foreach region Ovaln-t Probability Figure 1: Stage 1
6.10888283COVID-19 symptoms alert machine (CSAM) scanners
US - 12.01.2021
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
 Appl.No 16917896 Applicant Boonsieng Benjauthrit Inventor Boonsieng Benjauthrit

A COVID-19 Symptoms Alert Machine (CSAM) scanner, or apparatus, is described herein. This apparatus employs Artificial Intelligent (AI) technology in combination with the latest mobile device technology (viz. smart phone/smart watch) to quickly help track down people who have COVID-19 symptoms anywhere and anytime, isolate them, and professionally handle them, not allowing SARS-CoV-2 virus to spread. CSAM automatically measures body temperature and assesses lung conditions such as pulmonary fibrosis and B-lines (for asymptomatic people), and other current health vital information (CHVI), furnished by the participant, such as fever, sore throat, headache, and body ache to generate an alert signal when COVID-19 symptoms are found significant and to send it out to a COVID-19 control center. The alerted participant is then immediately required to go to the COVID-19 control center or be picked up by a special COVID-19 emergency vehicle for isolation and further evaluation and testing. If the testing turns out to be COVID-19 positive, the participant will be quarantined and treated appropriately according to COVID-19 protocol until he/she is tested COVID-19 negative. In the meantime, people who have been in close physical contact with this participant will be alerted and requested to be immediately checked for COVID-19 symptoms. If anyone is found to have COVID-19 symptoms, then he/she must go through the same protocol. The process is repeated until all people in the cluster are tested COVID-19 negative. This will ensure that SARS-CoV-2 virus for this cluster has been completely eliminated. A rapid deployment of this type of apparatus throughout communities where people tend to congregate such as superstores, supermarkets, and any other establishments, small or large, can help to contain the rapid spread of the disease, as well as to give more confidence to the general public. People, who pass through this apparatus without an alert signal, should feel more confident in carrying out their activities, though social distancing and other COVID-19 precautionary requirements should still be maintained. The concept can be further expanded to cover shopping malls, concert halls, sports arenas, and any other large events including highways and freeways with the help of mobile phone technologies, transponders, and other mobile devices. By working on the 0.6% (around 2 million infected people in the US as of June 2020) quickly and effectively, instead of on the 99.4% (330 million, the remaining population) by locking people at home and closing down all businesses and activities; we can save a significant amount of money and hassles. (A long lockdown can also lead to a collapse of our economy and can consequently lead to a worldwide calamity.) In this way the 99.4% will not be burdened with the virus problem and can live normally without having to take any test. It is probably the only effective approach in solving the COVID-19 problem at the moment because vaccines and known COVID-19 cures are not yet available. Even if SARS-CoV-2 vaccines are available presently, they may not be practical to implement economically and operationally in time to contain the virus worldwide due to the massive amount of people (viz. over 7 billion).

7.20220022818Assessment 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.

8.2021107308Method and System for prediction of COVID-19 through X-ray images using deep learning
AU - 18.11.2021
Int.Class G16H 50/00
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
 Appl.No 2021107308 Applicant BALIAH, N. TENSINGH Dr. Inventor BALIAH, N. TENSINGH
The system and method disclosed here relates to COVD-19 analysis through X-ray images using deep learning based approaches, and the deep learning approach used in this work is convolutional neural networks. The model used here to perform the classification of Covid-19 positive cases, normal case images and viral pneumonia case images. Different types of convolutional neural networks such as VGG-19, Resnet 50 and the inceptionV3 are implemented in this work. The evaluation parameters used for the analysis of the performance of the models are accuracy, sensitivity and specificity. The implementation of the work is done through google colab using the features of GPU. Evaluation parameters accuracy, specificity FIGURE 1 passing the X-Ray image dataset through a 2x2 pooling layer }/ 204 passing the X-Ray image dataset through 3 fully connected layers 206 method on the output obtained from above step 208 unction to obtain result based on classification COVID-19 and non-COVID into 210 Figure 2
9.20220108803METHOD FOR COVID-19 DETECTION TO SPREAD PREVENTION AND MEDICAL ASSISTANCE USING MACHINE LEARNING
US - 07.04.2022
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 17551376 Applicant Gaurav Indra Inventor Gaurav Indra

The present disclosure generally relates to a method for COVID-19 detection for spread prevention and medical assistance using machine learning comprises pre-processing a CT image set for removing noise using HU values; performing a region-based segmentation using a three-dimensional convolutional neural network model; classifying each segmented region by employing a classification model; and comparing classified segmented region with a dataset for detecting COVID-19 using a machine learning approach thereby generating an alert about the COVID-19 positive person.

10.2021103340AI BASED METHOD OF EXAMINING THE STATE OF LUNGS WITH SUSPECTED COVID-19 PATIENTS
AU - 15.07.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 2021103340 Applicant Kaur, Karamjeet Inventor Kaur, Karamjeet
AI-BASED METHOD FOR EXAMINING THE STATE OF THE LUNGS WITH SUSPECTED COVID-19 PATIENTS Aspects of the present disclosure relate to an artificial intelligence-based method (100) for examining state of lungs of a COVID-19 suspected patient. The said method comprises of: collecting (102), a plurality of image of computer tomography (CT) of normal lungs, lung's infected with tumor and lungs infected with COVID-19, classification (104) of the plurality of images into three subsets after collection (102), forming (106) a sample set after the classification (104) of the plurality of images, training (108) of a plurality of convolution neural network such as ImageNet, LeNet, VGGNet 16 and AlexNet with a transfer learning method, inputting (110) the sample set into the trained (108) convolution neural network for obtaining four classifiers, integrating (112) the four classifiers with an ensemble learning method for obtaining an ensemble classifier model for assessing the state of lungs of the COVID-19 suspected patient. (FIG. 1 will be the reference figure) aippiican name: rage i 011 Fig. 1 Flowchart of an artificial intelligence-based method for examining state of lungs of a COVID-19 suspected patient.
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