Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

The present invention provides a system and method for recognizing and segregating chicks by sex according to their feathers pattern. The system and method are based on the instinctive reaction of chicks to spread their wings in order to maintain stability in response to instantaneous disorientation, for example, due to a sudden change in spatial movement. The present invention further features a weighing system and a system for detecting external body defects of chicks for assessing chicks' health.

According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Systems and methods for aiding a clinician in the proper positioning, placing or otherwise locating of a non-contact detector component of a non-contact patient monitoring system are described. The systems and methods may employ a targeting aid superimposed on a display screen component of the non-contact patient monitoring system, the targeting aid being designed to assist the clinician in properly locating the non-contact detector for proper and accurate functioning of the non-contact patient monitoring system. The systems and methods described herein may also employ a bendable mounting arm to which the non-contact detector is attached such that the non-contact detector can be easily moved into the proper location when used in conjunction with the targeting aid superimposed on the display.

The present disclosure relates to the generation of artificial MRI images of the liver. The disclosure also relates to a method, a system and a computer program product for generating MRI images of the liver without contract enhancement.

A system for a thermal monitoring security camera is disclosed, including at least one camera positioned to monitor a facility. An image processing module receives imagery from the at least one camera and analyzes the imagery to determine, via a facial recognition module, an identity of an individual within the imagery and the presence or absence of a mask on the face of the individual. At least one thermal camera monitors the temperatures of each individual sensed by the thermal camera. An alert module transmits an alert if the temperature is above a threshold temperature. A security system provides access or prevent access to the facility.

A diagnostic method for determining if a patient is viable candidate for a surgical procedure to permanently eliminate migraine headache pain. The method includes determining if a patient's pain is a migraine pain condition or pain from another medical condition and determining an anatomical location of the determined migraine pain condition. Once the anatomical location is determined, the method correlates the determined anatomical location of the migraine pain condition to a root cause nasal/sinus location and determines a root cause nasal/sinus condition that is causing the patient's migraine pain. Once these diagnostic procedures are complete, the patient may be scheduled for surgery to eliminate the root cause nasal/sinus condition, and thus, permanently eliminate the migraine headache pain.

In one embodiment, an MRI apparatus includes: processing circuitry configured to: set a first pulse sequence and a second pulse sequence, wherein, in the first pulse sequence, a first gradient pulse is applied between two adjacent refocusing pulses, and, in the second pulse sequence, a second gradient pulse being different in pulse shape from the first gradient pulse is applied between two adjacent refocusing pulses, wherein: the scanner is configured to acquire first signals and second signals; and the processing circuitry is configured to generate at least one first image and at least one second image; and calculate a T2 value of a body fluid of the object from the at least one first image and the at least one second image in such a manner that influence of movement including diffusion of the body fluid is removed.

Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

A method for non-invasive quantification of organ fat using a magnetic resonance approach includes: constructing a detection system; connecting a detection area; detection system startup; acquiring data; analyzing data; and performing horizontal data analysis. An external computer, a radio frequency (RF) subsystem, and a portable magnet module are used to construct a system for non-invasive quantification of organ fat based on low-field nuclear magnetic resonance (LF-NMR,), which causes no damage, and achieves accurate and non-invasive quantification of organ fat. Specific pulse sequences are used to excite nuclear spin in a target region to generate LF-NMR, so as to achieve “one-click” detection, which is used for fast screening of related diseases such as non-alcoholic fatty liver disease (NAFLD). The system has accurate quantification, and is easy to operate without constraints of operator qualifications.