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Analysis

1 / 4,843
1.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

2.12274503Myopia ocular predictive technology and integrated characterization system
US - 15.04.2025
Int.Class A61B 3/14
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
14Arrangements specially adapted for eye photography
 Appl.No 18778027 Applicant COGNITIVECARE INC. Inventor Venkata Narasimham Peri

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

3.20210097682Disease characterization and response estimation through spatially-invoked radiomics and deep learning fusion
US - 01.04.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 17038934 Applicant Case Western Reserve University Inventor Anant Madabhushi

Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

4.WO/2023/059663SYSTEMS AND METHODS FOR ASSESSMENT OF BODY FAT COMPOSITION AND TYPE VIA IMAGE PROCESSING
WO - 13.04.2023
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2022/045706 Applicant THE BROAD INSTITUTE, INC. Inventor KHERA, Amit
The subject matter disclosed herein relates to utilizing the silhouette of an individual to measure body fat volume and distribution. Particular examples relates to providing a system, a computer-implemented method, and a computer program product to utilize a binary outline, or silhouette, to predict the individual's fat depot volumes with machine learning models.
5.20230132247APPARATUS AND METHODS FOR MACHINE LEARNING TO IDENTIFY AND DIAGNOSE INTRACRANIAL HEMORRHAGES
US - 27.04.2023
Int.Class A61B 5/02
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
02Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
 Appl.No 17508993 Applicant Benjamin Steven Hopkins Inventor Benjamin Steven Hopkins

In some embodiments, an apparatus includes providing a representation of a set of digital medical images to a first machine learning model to define a feature vector associated with a presence of an intracranial hemorrhage. A representation of the set of digital medical images is provided to a second machine learning model to define a second feature vector associated with a volume of the intracranial hemorrhage. Using a third machine learning model, a set of EMRs associated with risk factors for a predefined indication is analyzed to define a third feature vector. The first, second and third feature vectors are provided as inputs to a fourth machine learning model to determine a metric associated with an applicability of a specific treatment associated with a predefined indication. An alert is sent to relevant healthcare providers and relevant tests, procedures or bloodwork are ordered for the predefined indication.

6.20200202436Method and system using machine learning for prediction of stocks and/or other market instruments price volatility, movements and future pricing by applying random forest based techniques
US - 25.06.2020
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16783457 Applicant Dhruv Siddharth Krishnan Inventor Dhruv Siddharth Krishnan

A method for providing stock predictive information by a cloud-based computing system implementing a random forest algorithm via a machine learning model by receiving a set of stock data from multiple sources of stock data wherein the set of stock data at least comprises stock prices at the open and close of a market, changes in stock prices during the open and close of a market, and real-time stock data; defining a range in time contained in a window defined of an initial selected month, a day or real-time period and an end of the selected month, day and real-time period; applying the random forest model to the set of stock data by creating multiple decision trees to predict a stock price in a quantified period, amount or percentage change in a stock price; and presenting the predicted stock price in a graphic user interface to an user.

7.3786855AUTOMATED DATA PROCESSING AND MACHINE LEARNING MODEL GENERATION
EP - 03.03.2021
Int.Class G06N 5/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
 Appl.No 19290076 Applicant ACCENTURE GLOBAL SOLUTIONS LTD Inventor HIGGINS LUKE
A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.
8.WO/2023/141277SYSTEMS AND METHODS FOR SKIN BIOMOLECULAR PROFILE ASSESSMENT USING ARTIFICIAL INTELLIGENCE
WO - 27.07.2023
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2023/011249 Applicant VANDERBILT UNIVERSITY Inventor MARASCO, Christina, C.
Skin biomolecular profile assessment methods and systems that can analyze the molecular composition of the skin using molecular-level, user-specific data to assess an individual's skin state and/or disease state are described herein. An example method includes receiving skin data associated with a subject, where the skin data includes a biomolecular profile. The method also includes inputting the skin data into a trained artificial intelligence (AI) model and receiving, from the trained AI model, a skin care prediction.
9.WO/2022/155555SYSTEMS AND METHODS FOR DERIVING HEALTH INDICATORS FROM USER-GENERATED CONTENT
WO - 21.07.2022
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 PCT/US2022/012645 Applicant MY LUA LLC Inventor CONWARD, Michael
The present disclosure relates to systems and methods for generating priority lists and/or predictions or identifications of root causes of acute or chronic conditions. In one exemplary embodiment, a method comprises aggregating data corresponding to a plurality of individuals, the data comprising, for each individual, user-generated content and/or biometric data; generating, from a machine learning model that utilizes the aggregated user-generated content and/or biometric data as input, one or more of a priority list for the plurality of individuals, or, for each individual, a prediction, diagnosis, or identification of one or more root causes of one or more acute or chronic conditions of the individual.
10.2624169Methods and apparatus for classifying a capnogram and predicting a cardiorespiratory disease using a capnogram
GB - 15.05.2024
Int.Class A61B 5/08
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
08Measuring devices for evaluating the respiratory organs
 Appl.No 202216572 Applicant TIDALSENSE LTD Inventor AMEERA PATEL
A method for classifying a capnogram comprising: obtaining a breath waveform from a capnogram S101 determining one or more expiratory or inspiratory transition points of the breath waveform S102, this may include transition points between baselines and plateaus; extracting features of the breath waveform using the transition points S103; and applying a trained machine learning model to the extracted features S104, wherein the trained machine learning model is configured to output a classification of the capnogram from the features. A method for training the machine learning model is also claimed using a plurality of labelled capnograms. The methods may split the capnogram into a plurality sections. The methods may also include using first order differentials of the breath waveform to determine if it is anomalous. The methods may also include identifying hump artefacts in the waveform using peak detection. The machine learning model my comprise at least one of, logistic regression, gradient boosting decision tree, a support-vector machine, and a random forest. Extracting features of the transition points may include determining the angle of the transition points. Apparatus and a computer readable medium for performing the methods are also claimed.
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