Disclosed is an antenna device for measuring biometric information by using a leaky wave. The antenna device according to an embodiment may include an antenna main body formed to surround at least some portion of the body having a target analyte. The antenna main body may include a plurality of transmission-side slots and a plurality of reception-side slots formed on a surface surrounding the body portion. An electromagnetic wave is excited inside the antenna main body may be radiated to an inside of the body portion through at least one of the plurality of transmission-side slots. Information on an analyte within the body portion may be sensed based on a frequency of an electromagnetic wave received through at least one of the plurality of reception-side slots via the body portion.

The present disclosure relates to noninvasive methods, devices, and systems for measuring various blood constituents or analytes, such as glucose. In an embodiment, a light source comprises LEDs and super-luminescent LEDs. The light source emits light at at least wavelengths of about 1610 nm, about 1640 nm, and about 1665 nm. In an embodiment, the detector comprises a plurality of photodetectors arranged in a special geometry comprising one of a substantially linear substantially equal spaced geometry, a substantially linear substantially non-equal spaced geometry, and a substantially grid geometry.

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.

The present disclosure is directed to systems and methods for measuring and analyzing pupillary psychosensory responses. An electronic device is configured to receive video data with at least two frames. The electronic device then locates one or more eye objects in the video data and determine pupil and iris sizes of the one or more eye objects. The electronic device determines the pupillary psychosensory responses of the one or more eye objects by tracking a ratio of pupil diameter to iris diameter throughout the video. Several metrics for the pupillary psychosensory responses can be determined (e.g., velocity of change of the ratio, peak to peak amplitude of the change in ratio over time, etc.). These metrics can be used as measures of an individual's cognitive ability and mental health in a single session or tracked throughout multiple sessions.

The disclosure provides methods of managing opioid therapy, particularly, for pain management. The methods comprise determining in a subject, for example, a subject who has received an opioid treatment, pupillary unrest in ambient light (PUAL). Low values of PUAL can be used to identify patients at risk for opioid side-effects, such as opioid-related respiratory depression (OIRD), and who warrant attention to prevent such side effects. Accordingly, the methods include monitoring the patients having low values of PUAL for signs of adverse side-effects and/or limiting or avoiding administration of opioids.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

The present disclosure relates to a system for providing customized cosmetics by using genetic information, the system comprising: a terminal that receives user information related to a user; a genetic/medical information server that stores genetic information related to the user; a skin meter that acquires skin information on the user; and a cosmetic manufacturing system that manufactures cosmetics by discharging at least one cosmetic material, wherein the cosmetic manufacturing system can select a cosmetic material to be discharged on the basis of at least one among the user information, the genetic information, and the skin information.

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.

This disclosure is directed to systems and techniques for detecting change in patient health and if a change in patient health is detected, direct a medical device to generate for display output indicating the detection of the change in patient health. An example medical system or technique applies a model to values of configurable settings that are programmed into detection logic of a medical device; based on the application, determine whether modified values of the configurable settings, when implemented by the detection logic, would change a determination, by the medical device, regarding whether sensed physiological activity is indicative of cardiac episode for a patient; and in response to a determination that the modified values would change the determination regarding whether the sensed physiological activity is indicative of the cardiac episode for the patient, generate output data indicative of the modified values for the configurable settings for the medical device.

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.