A sensing system for sensing a potential complication at a venous catheter site. The system includes a sensor module for attachment at the site of the catheter. The sensor module includes a pressure sensor configured to generate pressure data representing measured pressure at the site of the catheter; a temperature sensor configured to generate temperature data representing measured temperature at the site of the catheter; and two pairs of bio impedance electrodes that generate bioelectrical signals representing bioelectrical activity at the site of venous catheter and a transmitter for transmitting the pressure, temperature data and bio impedance data. The system also includes a computing device configured to receive the response signal that includes the generated pressure, temperature and bio impedance data; and transmit the pressure temperature and bio impedance data to a user device for comparing the generated pressure temperature bio impedance data to threshold values indicative of intravenous complications.

A method of analyzing thoracic insufficiency syndrome (TIS) in a subject by performing quantitative dynamic magnetic resonance imaging (QdMRI) analysis. The QdMRI analysis includes performing four-dimensional (4D) image construction of a TIS subject's thoracic cavity. The 4D image includes a sequence of two dimensional (2D) images of the TIS subject's thoracic cavity over a respiratory cycle of the TIS subject. The QdMRI analysis also includes segmenting a region of interest (ROI) within the 4D image, determining TIS measurements within the ROI, comparing the TIS measurements to normal measurements determined from ROIs in 4D images of the thoracic cavities of normal subjects that are not afflicted by TIS, and outputting quantitative markers indicating deviation of the thoracic cavity of the TIS subject relative to the thoracic cavities of the normal subjects.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

The present disclosure relates to noninvasive methods for detecting liver fibrosis. Disclosed herein are noninvasive liver fibrosis detection methods that use Doppler Ultrasound devices and a physics-based machine learning method. Further disclosed herein are methods for detecting liver fibrosis in a subject by detecting and measuring the presence of a shift in the frequency of blood flow in the hepatic vein as compared to the frequency of blood flow in the portal vein.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging and ancillary information to the ultrasound imaging. At least two quantitative measurements of a subject, including at least one measurement taken using ultrasound imaging, as part of quantified information can be identified. One of the quantitative measurements can be compared to a first predetermined standard, included as part of ancillary information to the quantified information, in order to identify a first initial value. Further, another of the quantitative measurements can be compared to a second predetermined standard, included as part of the ancillary information, in order to identify a second initial value. Subsequently, the quantitative information can be correlated with the ancillary information using the first initial value and the second initial value to determine a final value that is predictive of a disease state of the subject.

The present invention provides a device for non-invasive monitoring and/or detection of diabetes in a subject based on detection of volatile organic compounds (VOCs) in the exhaled breath of a subject. The device comprises a functionalized carbon nanotube-based array sensor which can reversibly bind VOCs, which alters the electrical conductivity of the sensor array, which can be interpreted to monitor and/or diagnose diabetes.

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

This document discusses, among other things, apparatus, systems, or methods to efficiently collect heart sound data, including detecting first heart sound information of a heart of a patient using a heart sound sensor in a first, low-power operational mode, and detecting second heart sound information of the heart using the heart sound sensor in a separate second, high-power operational mode. The operational mode of the heart sound sensor can be controlled using physiologic information from the patient, including heart sound information, information about a heart rate of the patient, or other physiologic information from the patient that indicates worsening heart failure.

Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

Provided are systems for measuring an electrocardiogram (ECG) using a wearable device. An example system includes the wearable device. The wearable device has a means for recording an electrical signal from a single wrist of a patient. The wearable device also has a means for detecting a pulse of the patient and recording a photoplethysmogram (PPG) signal, via a PPG optical sensor associated with the wearable device. The wearable device further has a means for generating the electrical signal segments being time-locked to the PPG signal by utilizing the PPG signal as a reference signal. Furthermore, the wearable device has a means for summing the electrical signal segments in a given time period and dividing by the number of segments to produce an average ECG waveform.