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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

1.20210327055SYSTEMS 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.

2.10888283COVID-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).

3.2021100784Covid19 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
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
4.20210330269RISK 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.

5.2021107308Method 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
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
6.2021102880A NOVEL SYSTEM FOR COVID-19 PREDICTION IN CHEST RADIOGRAPHY IMAGES USING HYBRID QUANTUM MASK R-CNN MODEL
AU 17.06.2021
Int.Class G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
Appl.No 2021102880 Applicant Meka, James Stephen PROF Inventor
Developing a precise model to anticipate COVID-19 via Chest Radiography Images is required to help in preliminary diagnosis. In Pattern Recognition and Image Classification, Convolutional Neural Networks is one of the most widely used and efficient deep learning models. The Mask R-CNN (Regional-Convolutional Neural Networks), a segment-based image classification algorithm, achieves superior results in detecting a variety of diseases, including heart disease, dental disease, brain tumor, and pneumonia disease. Entanglement in quantum computing enables qubits that behave randomly to be perfectly correlated with one another. Specific complex problems can be solved more efficiently on quantum computers by utilizing quantum algorithms. The present invention disclosed herein is a Novel System for Covid-19 Prediction in Chest Radiography Images using Hybrid Quantum Mask R-CNN Model comprising of Random Quantum Circuits, Quantum Convolutional Neural Networks, and Modified Mask R CNN
7.2021104727DEVELOPMENT OF CNN SCHEME FOR COVID-19 DISEASE DETECTION USING CHEST RADIOGRAPH
AU 19.08.2021
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 2021104727 Applicant Biju, Roshima Inventor
DEVELOPMENT OF CNN SCHEME FOR COVID-19 DISEASE DETECTION USING CHEST RADIOGRAPH Aspects of the present disclosure relate to a method and system for covid-19 disease detection based on CNN model and X-Ray image classification comprises a database module (102), a processing module (104), a training and learning module (106), with the diagnostic parameters, wherein the method consists of collecting sample data using database module (102), by performing (204), adopting (206), dividing (208), augmenting (210), training (212), testing (214) and implementing (216) using processing module where selection is done using filtering method, wrapper method or embedded method where the node is computerized device, mobile or computer handset along with Rectified Linear Unit (ReLU) used in activation function wit convolutional layer to increase non-linearity in test image. (FIG. 2 will be the reference figure) ofX-rayimages 202 Perfirming feature selectionby processing module Adopting feature select onto foimm sample data Dividing the sample data according to certainmproportion Augnenintoincreasethedatasintancesinthe gaining data 210 Testingtheta Implemientinig t&e tained no"ranetwork21 Fig. 2 Flowchart of a method for covid-19 disease detection based on CNN model and X Ray image classification.
8.2021103578A Novel Method COVID -19 infection using Deep Learning Based System
AU 29.07.2021
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 2021103578 Applicant Eswaraiah, Rayachoti Inventor
Coronavirus illness has infected millions of individuals globally and is contaminating them at an alarming rate, putting a strain on the health system's capabilities. PCR screening is the analytic technique of choice for COVID-19 discovery. CT imaging has demonstrated its diagnostic competence in asymptotic patients, establishing it as a reliable diagnostic support tool for COVID-19. The high prevalence of COVID-19 contaminations in CT slices enables the tracking of illness progression using automated contamination segmentation techniques. Nevertheless, COVID-19 infection regions exhibit a great degree of heterogeneity in terms of scale, form, contrast, and concentration, posing a substantial challenge to the segmentation approach. The present invention provides an automated segmentation technique based on deep learning for the discovery and delineation of COVID-19 infections in CT images. Additionally, as compared to existing techniques, this suggested innovation will need less time and money to identify the infection and annotate the infection regions. This innovation will assist physicians in determining the course of covid-19 illness, as well as quantifying the disease's load and severity, by segmenting the lung organ from the CT scan as a region of concern and then segmenting the infections contained inside it.
9.2021100007Deep Learning Based System for the Detection of COVID-19 Infections
AU 11.02.2021
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 2021100007 Applicant Aravindan, Divya Preetha MISS Inventor
Deep Learning Based System for the Detection of COVID-19 Infections Coronavirus disease has spread to millions of people worldwide and has increasingly contaminated them, putting high pressure on the health system's facilities. PCR screening is the adopted diagnostic testing method for COVID-19 identification. Even in asymptotic patients, CT imaging has shown its ability to diagnose the disease, making it a trustworthy diagnostic support tool for COVID-19. In CT slices, the incidence of COVID-19 infections provides a high potential to support disease evolution tracking using automated infection segmentation methods. However, COVID-19 infection areas contain high variations in the homogeneity of scale, shape, contrast and intensity, which pose a significant challenge to the method of segmentation. This invention proposes an automated segmentation method for deep learning to detect and delineate the infections of COVID-19 in CT scans. Also this proposed invention will consume less time and less cost to detect the infection and in the process of annotating the areas of infection, compared with the existing approaches. This invention will help the doctors to determine the progression of the covid-19 disease, to measure the burden and seriousness of the disease by segmenting the lung organ from the CT scan as an area of concern, and then segmenting the infections within it.
10.2021104206PREDICTING COVID-19 INFECTED PATIENTS BASED ON MACHINE LEARNING IN HEALTHCARE MODEL
AU 12.08.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
Appl.No 2021104206 Applicant Azath, H. DR Inventor
PREDICTING COVID-19 INFECTED PATIENTS BASED ON MACHINE LEARNING IN HEALTHCARE MODEL Aspects of the present disclosure relate to a method (100) for prediction of COVID-19 based on machine learning. The invention provides a CT image-based quantitative analysis method (100) for COVID-19. The method (100) comprises the step of initially creating (102) a machine learning model. After the creating (102) the machine learning model, plurality of CT images are inputted into the machine learning model. After the images are inputted (104), generating (106) 3D focus data for each of the CT images. Based on the 3D focus data, a quantification factor is calculated (108) for each of the CT image. Based on this quantification it is analyzed (110) whether the patient is suffering from COVID-19 or not. The doctor can also visually observe the information such as the size, position of the lesion region. (FIG. 1 will be the reference figure) 00 y comparing the quantification the critical threshold value 110 FIG. 1. Flowchart of method based on machine learning for the detection of COVID-19 patients using CT imaging