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Analysis

1.20210343411DEEP LEARNING-BASED DIAGNOSIS AND REFERRAL OF DISEASES AND DISORDERS USING NATURAL LANGUAGE PROCESSING
US 04.11.2021
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 17136018 Applicant AI TECHNOLOGIES INC. Inventor Kang ZHANG

Disclosed herein are methods and systems for Artificial Intelligence (AI)-based methods for performing medical diagnosis of diseases and conditions. An automated natural language processing (NLP) system performs deep learning techniques to extract clinically relevant information from electronic health records (EHRs). This framework provides a high diagnostic accuracy that demonstrates a successful AI-based method for systematic disease diagnosis and management.

2.WO/2020/006495DEEP LEARNING-BASED DIAGNOSIS AND REFERRAL OF DISEASES AND DISORDERS USING NATURAL LANGUAGE PROCESSING
WO 02.01.2020
Int.Class G06F 17/28
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
28Processing or translating of natural language
Appl.No PCT/US2019/039955 Applicant AI TECHNOLOGIES INC. Inventor ZHANG, Kang
Disclosed herein are methods and systems for Artificial Intelligence (AI)-based methods for performing medical diagnosis of diseases and conditions. An automated natural language processing (NLP) system performs deep learning techniques to extract clinically relevant information from electronic health records (EHRs). This framework provides a high diagnostic accuracy that demonstrates a successful AI-based method for systematic disease diagnosis and management.
3.12274503Myopia 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.

4.WO/2025/010329MACHINE LEARNING-BASED METHODS AND SYSTEMS FOR PREDICTING RISK BASED ON MULTI-MODAL HEALTH DATA
WO 09.01.2025
Int.Class G16H 20/00
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
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
Appl.No PCT/US2024/036687 Applicant H. LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE, INC. Inventor GONZALEZ, Brian
An example method for predicting medical risk is described herein. The method includes receiving multi-modal health data for a subject, and inputting the multi-modal health data into a trained machine learning model. The method also includes predicting, using the trained machine learning model, a medical risk for the subject. The multi-modal health data for the subject includes patient-generated health data (PGHD), patient-reported outcome (PRO) data, and imaging data.
5.20220180975METHODS 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.

6.20230132247APPARATUS 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.

7.20240321451System And Method For Diagnostic Coding
US 26.09.2024
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 18428802 Applicant Bruce Wayne FallHowe Inventor Bruce Wayne FallHowe

A computer implemented coding system for mandating correct medical diagnostic coding by a provider, comprises program code executable to receive patient encounter medical data associated with a patient. Medical elements performed during the patient encounter are compared and matched with up-to-date guidelines that include medical guideline, derived from a stored database of diagnostic code requirements determined via a deep machine learning/artificial intelligence acquisition of up-to-date medial data, by which a diagnosis is made and published for consideration by a treating provider. The matched guideline is published on a display screen visible to the treating provider where it is accepted or refused in favor of the treating providers alternative diagnosis. If accepted, an insurance code is determined and submitted for payment. If not, a list of missing medical procedures associated with the provider's diagnosis is determined and ordered before an insurance code may be assigned.

8.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.
9.WO/2024/243364SUBARACHNOID HEMORRHAGE DETECTION AND RISK STRATIFICATION WITH MACHINE LEARNING-BASED ANALYSIS OF MEDICAL IMAGES
WO 28.11.2024
Int.Class G16H 30/20
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
30ICT specially adapted for the handling or processing of medical images
20for handling medical images, e.g. DICOM, HL7 or PACS
Appl.No PCT/US2024/030654 Applicant MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH Inventor FREEMAN, William D.
Subarachnoid hemorrhage (SAH) is detected using a suitably trained machine learning model. The machine learning model may take medical images as an input. Additionally or alternatively, the machine learning model may take quantified SAH blood volume (qvSAH) data and patient health data as an input. In some examples, the machine learning model may generate classified feature data indicating a risk stratification, severity of illness, and/or prognosis. A report may be generated, which may include suggestions for clinical decision support, interventions based on the severity of illness, or triaging for the patient.
10.20210097682Disease 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.