A medical system according to embodiments of the present invention includes at least one sensor configured to monitor physiological status of a patient and to generate sensor data based on the physiological status, a user interface device, a processor communicably coupled to the user interface device, the processor configured to: present via the user interface device an array of two or more possible input elements, the input elements each comprising a class of patients or a diagnosis and treatment pathway, receive a selected input element based on a user selection among the two or more possible input elements, acquire the sensor data and process the sensor data to generate physiological data, and present via the user interface screen the physiological data according to a template that is customized for the selected input element.

A patient support structure for assisting cardiopulmonary resuscitation (CPR) treatment of a patient is described. The patient support structure includes a base frame, one or more patient support sections supported by the base frame, at least one tilt adjuster coupled to at least one of the patient support sections and configured to tilt the at least one of the patient support sections, around a transverse axis of the patient support structure, to a tilt angle, and a chest compression (CC) device mount disposed on at least one of the patient support sections and configured to adjustably secure a CC device to the patient support structure. The tilt angle may be a target tilt angle and the patient support structure may further include a processor configured to determine the target tilt angle based on at least one of sensor input and user input.

Wireless systems and methods are provided for protecting vital organs from blood flow loss, and for physiological monitoring of a subject in various settings. The systems and methods permit the coordinated delivery of various treatment protocols involving inflation or deflation of cuffs to multiple limbs of the subject.

Examples of systems and methods described herein may classify agonal breathing in audio signals produced by a user using a trained neural network. Examples may include a smart device that may request medical assistance if an agonal breathing event is classified.

Disclosed is a system designed for providing immediate treatment in medical emergency situations effecting respiratory, cardiac, and/or central nervous system functions. For non-limiting examples, the system may also be used for conditions such as trauma and chronic conditions. The main goal of the system is to provide a person who experiences a medical emergency with personalized treatment that is as close as possible to the treatment that he/she would receive in an emergency room beginning from the moment the event occurs.

Many people have died in their homes in their sleep while suffering from COVID-19 infection. This invention proposes a patient and caregiver bracelet system by which patient breathing and heart rate status declines are monitored through pulse oximetry connected to their bracelets and caregivers sleeping in the same house are alerted via their own caregiver bracelets. The underlying communications signaling can be sent via wireless digital technology and also via radio frequency.

Embodiments of the disclosure provide systems and methods for detecting a medical condition of a subject. The system includes a communication interface configured to receive a sequence of images acquired from the subject by an image acquisition device and an end-to-end multi-task learning model. The end-to-end multi-task learning model includes an encoder, a Convolutional Recurrent Neural Network (ConvRNN), and at least one of a decoder and a classifier. The system further includes at least one processor configured to extract feature maps from the images using the encoder, capture contextual information between adjacent images in the sequence using the ConvRNN, and detect medical condition of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain a region of interest indicative of the medical condition based on the extracted feature maps.

A non-invasive method of estimating intra-cranial pressure (ICP). The method including the steps of: a. non-invasively measuring pressure pulses in an upper body artery; b. determining central aortic pressure (CAP) pulses that correspond to these measured pressure pulses; c. identifying features of the ICP wave which denote cardiac ejection and wave reflection from the cranium, including Ejection Duration (ED) and Augmentation Index of Pressure (PAIx); d. non-invasively measuring flow pulses in a central artery which supplies blood to the brain within the cranium; e. identifying features of the measured cerebral flow waves which denote cardiac ejection and wave reflection from the cranium as Flow Augmentation Index (FAIx); f. calculating an ICP flow augmentation index from the measured central flow pulses; g. comparing the calculated ICP pressure augmentation index (PAIx) and flow augmentation index (FAIx) to measure (gender-specific) pressure and flow augmentation data indicative of a measured ICP to thereby estimate actual ICP; and h. noting any disparity between ED measured for pressure waves and ED measured for flow.

There is provided a system (100) for assessing delirium in a subject. The system includes a neural activity assessment module (102) for assessing neural activity data associated with the subject. The system also includes a delirium cause assessment module (104) for assessing data relating to at least one factor of a plurality of factors which contribute to the cause of delirium. The system also includes an intervention determination module (106) for determining, based on the assessment performed by the delirium cause assessment module, at least one intervention for reducing the contribution made by the at least one factor. A computer-implemented method and an apparatus are also disclosed.

Embodiments of the disclosure provide systems and methods for detecting an intracerebral hemorrhage (ICH). The system includes a communication interface configured to receive a sequence of image slices and an end-to-end multi-task learning model. The sequence of image slices is the head scan images of a subject acquired by an image acquisition device. The end-to-end multi-task learning model includes an encoder, a bi-directional Convolutional Recurrent Neural Network (ConvRNN), a decoder, and a classifier. The system further includes at least one processor configured to extract feature maps from each image slice using the encoder, capture contextual information between adjacent image slices using the bi-directional ConvRNN, and detect the ICH of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain an ICH region based on the extracted feature maps of the image slice.