G06T2207/30004

TEMPORAL DISEASE STATE COMPARISON USING MULTIMODAL DATA

A system and method for visualizing and annotating temporal trends of an abnormal condition in patient data. A classification and visualization module detects one or more conditions in one or more images, e.g. X-ray images, and visualizes the condition on the image. A temporal disease state extraction module analyzes text, e.g. radiology reports, for indications of a change in the condition. A multimodal disease state comparison module fuses the extracted data into a compact representation of the condition changes over time.

Method For Diagnosing Alzheimer's Disease Spectrum Neurocognitive Disorders From Tear Sample, And Solution And Diagnostic Kit Useful In The Method

The invention relates to an aqueous solution of AuCl.sub.3×2H.sub.2O or HAuCl.sub.4×4H.sub.2O, ZnCl.sub.2 or ZnSO.sub.4, and AgNO.sub.3 having an Au.sup.3+ concentration of 0.8 mM-1.6 mM, a Zn.sup.2+ concentration of 15 μM-50 μM, and an Ag.sup.+ concentration of 5 μM-50 μM. The invention extends to a kit comprising a solution of the invention, a method of preparing said solution, and the use of a solution of the invention, a solution prepared by a method of the invention, or a kit of the invention in predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum. The invention extends to a method for predicting and/or diagnosing and/or monitoring a neurocognitive disorder of the Alzheimer's disease spectrum in a patient comprising contacting a tear sample from the patient normalised for protein concentration with a solution of the invention; applying an amount of the tear sample thus obtained to a surface; allowing the tear sample to dry; and drawing a conclusion based on the pattern of the thus obtained dried tear sample, whether the patient is at risk of or suffers from a neurocognitive disorder of the Alzheimer's disease spectrum.

AUTOMATIC PRESSURE ULCER MEASUREMENT

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.

Radiomic Biomarker Determination Method and System for Assessment of the Risk of Metabolic Diseases
20230237646 · 2023-07-27 ·

A radiomic biomarker determination method and system for assessment of the risk of metabolic diseases. The method includes: obtaining abdominal & pelvic volumetric computed tomography (CT) scan from the given subject; determining the fat area to be analyzed from the CT scan, separating visceral fat using an image segmentation method, and normalizing the visceral fat area under physical scale; extracting N imaging features of the visceral fat; selecting n optimal imaging features from the N candidate features; dividing the normalized visceral fat area into multiple visceral fat blocks with equal thickness; extracting n corresponding optimal imaging features from each visceral fat block, named as block imaging features; and determining the representative visceral fat block from the candidate blocks and taking the representative visceral fat block and the (block) imaging features extracted from the representative visceral fat block as radiomic biomarkers.

MOBILE SENSING SYSTEM FOR CROP MONITORING

Described herein are mobile sensing units for capturing raw data corresponding to certain characteristics of plants and their growing environment. Also described are computer devices and related methods for collecting user inputs, generating information relating to the plants and/or growing environment based on the raw data and user inputs, and displaying same.

IMAGE PROCESSING APPARATUS, METHOD AND PROGRAM, LEARNING APPARATUS, METHOD AND PROGRAM, AND DERIVATION MODEL
20230022549 · 2023-01-26 · ·

An image processing apparatus includes at least one processor, and the processor derives three-dimensional coordinate information that defines a position of a structure in a tomographic plane from a tomographic image including the structure, and that defines a position of an end part of the structure outside the tomographic plane in a direction intersecting the tomographic image.

Bio-sensing based monitoring of health

In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

METHOD AND APPARATUS FOR ANALYZING A PRODUCT, TRAINING METHOD, SYSTEM, COMPUTER PROGRAM, AND COMPUTER-READABLE STORAGE MEDIUM

A method of analyzing a product includes performing an anomaly detection on a received image using an autoencoder, wherein the autoencoder includes at least one first neural network trained based on a first set of training images, and the first set of training images includes a plurality of training images each showing a corresponding defect-free product; determining, using a binary classifier, whether or not a defect is present based on a result of the anomaly detection; performing defect detection on the received image using a defect detector, wherein the defect detector includes a third neural network trained based on a one third set of training images, and the third set of training images includes a plurality of training images each showing a corresponding defective product; and evaluating a result based on a weighting of the results of the anomaly detection, the defect detection, and the binary classifier.

METHOD AND SYSTEM FOR DETERMINING A POSE OF AT LEAST ONE OBJECT IN AN OPERATING THEATRE
20230026585 · 2023-01-26 ·

The invention relates to a method and a system for determining a pose of at least one object in an operating theatre, in a reference coordinate system of a pose detection device of a surgical microscope, involving the determination of the pose of the object by way of a movably arranged microscope-external pose detection device in a first coordinate system, the first coordinate system being a coordinate system that is arranged to be stationary relative to the operating theatre, the determination of the pose of the reference coordinate system by the non-stationary microscope-external pose detection device in the first coordinate system, and the transformation of the pose of the object from the first coordinate system into the reference coordinate system of the pose detection device of the surgical microscope.

Method and apparatus for automatic determination of object and background region of interest for real-time automatic dose rate control in dynamic imaging systems

A method of imaging includes obtaining a first image including projection data representing an intensity of X-rays detected by a plurality of detectors at a first X-ray exposure setting, the X-rays being emitted from an X-ray source; based on a detection result of a first object in the first image: determining a background region of interest (ROI) around the first object, the background ROI including background ROI pixels having a first intensity value corresponding to the intensity of the X-rays; and converting, for each pixel of the background ROI pixels, the first intensity values of the background ROI pixels to a normalized X-ray attenuation factor; and determining a second X-ray exposure setting for use in obtaining a second image based on the background ROI pixels converted to the normalized X-ray attenuation factor.